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63 results (View BibTeX file of all listed publications)

2007

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HPLC analysis and pharmacokinetic study of quercitrin and isoquercitrin in rat plasma after administration of Hypericum japonicum thunb. extract.

Li, J., Wang, W., Zhang, L., Chen, H., Bi, S.

Biomedical Chromatography, 22(4):374-378, December 2007 (article)

Abstract
A simple HPLC method was developed for determination of quercitrin and isoquercitrin in rat plasma. Reversed-phase HPLC was employed for the quantitative analysis using kaempferol-3-O--d-glucopyranoside-7-O--l-rhamnoside as an internal standard. Following extraction from the plasma samples with ethyl acetate-isopropanol (95:5, v/v), these two compounds were successfully separated on a Luna C18 column (250 × 4.6 mm, 5 µm) with isocratic elution of acetonitrile-0.5% aqueous acetic acid (17:83, v/v) as the mobile phase. The flow-rate was set at 1 mL/min and the eluent was detected at 350 nm for both quercitrin and isoquercitrin. The method was linear over the studied ranges of 50-6000 and 50-5000 ng/mL for quercitrin and isoquercitrin, respectively. The intra- and inter-day precisions of the analysis were better than 13.1 and 13.2%, respectively. The lower limits of quantitation for quercitrin and isoquercitrin in plasma were both of 50 ng/mL. The mean extraction recoveries were 73 and 61% for quercitrin and i soquercitrin, respectively. The validated method was successfully applied to pharmacokinetic studies of the two analytes in rat plasma after the oral administration of Hypericum japonicum thunb. ethanol extract.

ei

Web DOI [BibTex]

2007

ei Li, J., Wang, W., Zhang, L., Chen, H., Bi, S. HPLC analysis and pharmacokinetic study of quercitrin and isoquercitrin in rat plasma after administration of Hypericum japonicum thunb. extract. Biomedical Chromatography, 22(4):374-378, December 2007 (article)

Web DOI [BibTex]

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Graph sharpening plus graph integration: a synergy that improves protein functional classification

Shin, HH., Lisewski, AM., Lichtarge, O.

Bioinformatics, 23(23):3217-3224, December 2007 (article)

ei

Web DOI [BibTex]

ei Shin, HH., Lisewski, AM., Lichtarge, O. Graph sharpening plus graph integration: a synergy that improves protein functional classification Bioinformatics, 23(23):3217-3224, December 2007 (article)

Web DOI [BibTex]

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A Tutorial on Spectral Clustering

von Luxburg, U.

Statistics and Computing, 17(4):395-416, December 2007 (article)

Abstract
In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

ei

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ei von Luxburg, U. A Tutorial on Spectral Clustering Statistics and Computing, 17(4):395-416, December 2007 (article)

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A Tutorial on Kernel Methods for Categorization

Jäkel, F., Schölkopf, B., Wichmann, F.

Journal of Mathematical Psychology, 51(6):343-358, December 2007 (article)

Abstract
The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial neural networks and reinforcement learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics and functional analysis. Much of this research is potentially interesting for psychological theories of learning and categorization but also hardly accessible for psychologists. Here, we provide a tutorial introduction to a popular class of machine learning tools, called kernel methods. These methods are closely related to perceptrons, radial-basis-function neural networks and exemplar theories of catego rization. Recent theoretical advances in machine learning are closely tied to the idea that the similarity of patterns can be encapsulated in a positive definite kernel. Such a positive definite kernel can define a reproducing kernel Hilbert space which allows one to use powerful tools from functional analysis for the analysis of learning algorithms. We give basic explanations of some key concepts—the so-called kernel trick, the representer theorem and regularization—which may open up the possibility that insights from machine learning can feed back into psychology.

ei

PDF Web DOI [BibTex]

ei Jäkel, F., Schölkopf, B., Wichmann, F. A Tutorial on Kernel Methods for Categorization Journal of Mathematical Psychology, 51(6):343-358, December 2007 (article)

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A semigroup approach to queueing systems

Haji, A., Radl, A.

Semigroup Forum, 75(3):610-624, December 2007 (article)

Abstract
We prove asymptotic stability of the solutions of equations describing a simple queueing system consisting of two machines separated by a finite storage buffer. Following an approach by G. Gupur, we apply the theory of C0-semigroups and spectral theory of positive operators.

ei

PDF DOI [BibTex]

ei Haji, A., Radl, A. A semigroup approach to queueing systems Semigroup Forum, 75(3):610-624, December 2007 (article)

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Point-spread functions for backscattered imaging in the scanning electron microscope

Hennig, P., Denk, W.

Journal of Applied Physics , 102(12):1-8, December 2007 (article)

Abstract
One knows the imaging system's properties are central to the correct interpretation of any image. In a scanning electron microscope regions of different composition generally interact in a highly nonlinear way during signal generation. Using Monte Carlo simulations we found that in resin-embedded, heavy metal-stained biological specimens staining is sufficiently dilute to allow an approximately linear treatment. We then mapped point-spread functions for backscattered-electron contrast, for primary energies of 3 and 7 keV and for different detector specifications. The point-spread functions are surprisingly well confined (both laterally and in depth) compared even to the distribution of only those scattered electrons that leave the sample again.

ei pn

Web DOI [BibTex]

ei pn Hennig, P., Denk, W. Point-spread functions for backscattered imaging in the scanning electron microscope Journal of Applied Physics , 102(12):1-8, December 2007 (article)

Web DOI [BibTex]

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Accurate Splice site Prediction Using Support Vector Machines

Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.

BMC Bioinformatics, 8(Supplement 10):1-16, December 2007 (article)

Abstract
Background: For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks. Results: In this work we consider Support Vector Machines for splice site recognition. We employ the so-called weighted degree kernel which turns out well suited for this task, as we will illustrate in several experiments where we compare its prediction accuracy with that of recently proposed systems. We apply our method to the genome-wide recognition of splice sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, and Homo sapiens. Our performance estimates indicate that splice sites can be recognized very accurately in these genomes and that our method outperforms many other methods including Markov Chains, GeneSplicer and SpliceMachine. We provide genome-wide predictions of splice sites and a stand-alone prediction tool ready to be used for incorporation in a gene finder. Availability: Data, splits, additional information on the model selection, the whole genome predictions, as well as the stand-alone prediction tool are available for download at http:// www.fml.mpg.de/raetsch/projects/splice.

ei

PDF DOI [BibTex]

ei Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G. Accurate Splice site Prediction Using Support Vector Machines BMC Bioinformatics, 8(Supplement 10):1-16, December 2007 (article)

PDF DOI [BibTex]

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A unifying framework for robot control with redundant DOFs

Peters, J., Mistry, M., Udwadia, F., Nakanishi, J., Schaal, S.

Autonomous Robots, 24(1):1-12, October 2007 (article)

Abstract
Recently, Udwadia (Proc. R. Soc. Lond. A 2003:1783–1800, 2003) suggested to derive tracking controllers for mechanical systems with redundant degrees-of-freedom (DOFs) using a generalization of Gauss’ principle of least constraint. This method allows reformulating control problems as a special class of optimal controllers. In this paper, we take this line of reasoning one step further and demonstrate that several well-known and also novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sarcos Master Arm robot for some of the derived controllers. The suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equations, both with or without external constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

ei

PDF PDF DOI [BibTex]

ei Peters, J., Mistry, M., Udwadia, F., Nakanishi, J., Schaal, S. A unifying framework for robot control with redundant DOFs Autonomous Robots, 24(1):1-12, October 2007 (article)

PDF PDF DOI [BibTex]

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The Need for Open Source Software in Machine Learning

Sonnenburg, S., Braun, M., Ong, C., Bengio, S., Bottou, L., Holmes, G., LeCun, Y., Müller, K., Pereira, F., Rasmussen, C., Rätsch, G., Schölkopf, B., Smola, A., Vincent, P., Weston, J., Williamson, R.

Journal of Machine Learning Research, 8, pages: 2443-2466, October 2007 (article)

Abstract
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not realized, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.

ei

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ei Sonnenburg, S., Braun, M., Ong, C., Bengio, S., Bottou, L., Holmes, G., LeCun, Y., Müller, K., Pereira, F., Rasmussen, C., Rätsch, G., Schölkopf, B., Smola, A., Vincent, P., Weston, J., Williamson, R. The Need for Open Source Software in Machine Learning Journal of Machine Learning Research, 8, pages: 2443-2466, October 2007 (article)

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On the Representer Theorem and Equivalent Degrees of Freedom of SVR

Dinuzzo, F., Neve, M., De Nicolao, G., Gianazza, U.

Journal of Machine Learning Research, 8, pages: 2467-2495, October 2007 (article)

Abstract
Support Vector Regression (SVR) for discrete data is considered. An alternative formulation of the representer theorem is derived. This result is based on the newly introduced notion of pseudoresidual and the use of subdifferential calculus. The representer theorem is exploited to analyze the sensitivity properties of ε-insensitive SVR and introduce the notion of approximate degrees of freedom. The degrees of freedom are shown to play a key role in the evaluation of the optimism, that is the difference between the expected in-sample error and the expected empirical risk. In this way, it is possible to define a Cp-like statistic that can be used for tuning the parameters of SVR. The proposed tuning procedure is tested on a simulated benchmark problem and on a real world problem (Boston Housing data set).

ei

Web [BibTex]

ei Dinuzzo, F., Neve, M., De Nicolao, G., Gianazza, U. On the Representer Theorem and Equivalent Degrees of Freedom of SVR Journal of Machine Learning Research, 8, pages: 2467-2495, October 2007 (article)

Web [BibTex]

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Some observations on the masking effects of Mach bands

Curnow, T., Cowie, DA., Henning, GB., Hill, NJ.

Journal of the Optical Society of America A, 24(10):3233-3241, October 2007 (article)

Abstract
There are 8 cycle / deg ripples or oscillations in performance as a function of location near Mach bands in experiments measuring Mach bands’ masking effects on random polarity signal bars. The oscillations with increments are 180 degrees out of phase with those for decrements. The oscillations, much larger than the measurement error, appear to relate to the weighting function of the spatial-frequency-tuned channel detecting the broad- band signals. The ripples disappear with step maskers and become much smaller at durations below 25 ms, implying either that the site of masking has changed or that the weighting function and hence spatial-frequency tuning is slow to develop.

ei

PDF Web DOI [BibTex]

ei Curnow, T., Cowie, DA., Henning, GB., Hill, NJ. Some observations on the masking effects of Mach bands Journal of the Optical Society of America A, 24(10):3233-3241, October 2007 (article)

PDF Web DOI [BibTex]

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Mining complex genotypic features for predicting HIV-1 drug resistance

Saigo, H., Uno, T., Tsuda, K.

Bioinformatics, 23(18):2455-2462, September 2007 (article)

Abstract
Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: Even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly. Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch-and-bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors (NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature.

ei

Web DOI [BibTex]

ei Saigo, H., Uno, T., Tsuda, K. Mining complex genotypic features for predicting HIV-1 drug resistance Bioinformatics, 23(18):2455-2462, September 2007 (article)

Web DOI [BibTex]

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Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA

Waldert, S., Bensch, M., Bogdan, M., Rosenstiel, W., Schölkopf, B., Lowery, C., Eswaran, H., Preissl, H.

IEEE Transactions on Biomedical Engineering, 54(10):1867-1874, September 2007 (article)

Abstract
Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been na med Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems.

ei

PDF Web DOI [BibTex]

ei Waldert, S., Bensch, M., Bogdan, M., Rosenstiel, W., Schölkopf, B., Lowery, C., Eswaran, H., Preissl, H. Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA IEEE Transactions on Biomedical Engineering, 54(10):1867-1874, September 2007 (article)

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Overcomplete Independent Component Analysis via Linearly Constrained Minimum Variance Spatial Filtering

Grosse-Wentrup, M., Buss, M.

Journal of VLSI Signal Processing, 48(1-2):161-171, August 2007 (article)

Abstract
Independent Component Analysis (ICA) designed for complete bases is used in a variety of applications with great success, despite the often questionable assumption of having N sensors and M sources with N&#8805;M. In this article, we assume a source model with more sources than sensors (M>N), only L<N of which are assumed to have a non-Gaussian distribution. We argue that this is a realistic source model for a variety of applications, and prove that for ICA algorithms designed for complete bases (i.e., algorithms assuming N=M) based on mutual information the mixture coefficients of the L non-Gaussian sources can be reconstructed in spite of the overcomplete mixture model. Further, it is shown that the reconstructed temporal activity of non-Gaussian sources is arbitrarily mixed with Gaussian sources. To obtain estimates of the temporal activity of the non-Gaussian sources, we use the correctly reconstructed mixture coefficients in conjunction with linearly constrained minimum variance spatial filtering. This results in estimates of the non-Gaussian sources minimizing the variance of the interference of other sources. The approach is applied to the denoising of Event Related Fields recorded by MEG, and it is shown that it performs superiorly to ordinary ICA.

ei

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Feature Selection for Trouble Shooting in Complex Assembly Lines

Pfingsten, T., Herrmann, D., Schnitzler, T., Feustel, A., Schölkopf, B.

IEEE Transactions on Automation Science and Engineering, 4(3):465-469, July 2007 (article)

Abstract
The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products’ integrity can often only be checked in a final measurement. Troubleshooting can therefore be very tedious if not impossible in large assembly lines. In this paper we show that Feature Selection is an efficient tool for serial-grouped lines to reveal causes for irregularities in product attributes. We compare the performance of several methods for Feature Selection on real-world problems in mass-production of semiconductor devices. Note to Practitioners— We present a data based procedure to localize flaws in large production lines: using the results of final quality inspections and information about which machines processed which batches, we are able to identify machines which cause low yield.

ei

PDF Web DOI [BibTex]

ei Pfingsten, T., Herrmann, D., Schnitzler, T., Feustel, A., Schölkopf, B. Feature Selection for Trouble Shooting in Complex Assembly Lines IEEE Transactions on Automation Science and Engineering, 4(3):465-469, July 2007 (article)

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Gene selection via the BAHSIC family of algorithms

Song, L., Bedo, J., Borgwardt, K., Gretton, A., Smola, A.

Bioinformatics, 23(13: ISMB/ECCB 2007 Conference Proceedings):i490-i498, July 2007 (article)

Abstract
Motivation: Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical connections and the differences between these methods. In this article, we define a kernel-based framework for feature selection based on the Hilbert–Schmidt independence criterion and backward elimination, called BAHSIC. We show that several well-known feature selectors are instances of BAHSIC, thereby clarifying their relationship. Furthermore, by choosing a different kernel, BAHSIC allows us to easily define novel feature selection algorithms. As a further advantage, feature selection via BAHSIC works directly on multiclass problems. Results: In a broad experimental evaluation, the members of the BAHSIC family reach high levels of accuracy and robustness when compared to other feature selection techniques. Experiments show that features selected with a linear kernel provide the best classification performance in general, but if strong non-linearities are present in the data then non-linear kernels can be more suitable.

ei

Web DOI [BibTex]

ei Song, L., Bedo, J., Borgwardt, K., Gretton, A., Smola, A. Gene selection via the BAHSIC family of algorithms Bioinformatics, 23(13: ISMB/ECCB 2007 Conference Proceedings):i490-i498, July 2007 (article)

Web DOI [BibTex]

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Phenotyping of Chondrocytes In Vivo and In Vitro Using cDNA Array Technology

Zien, A., Gebhard, P., Fundel, K., Aigner, T.

Clinical Orthopaedics and Related Research, 460, pages: 226-233, July 2007 (article)

Abstract
The cDNA array technology is a powerful tool to analyze a high number of genes in parallel. We investigated whether large-scale gene expression analysis allows clustering and identification of cellular phenotypes of chondrocytes in different in vivo and in vitro conditions. In 100% of cases, clustering analysis distinguished between in vivo and in vitro samples, suggesting fundamental differences in chondrocytes in situ and in vitro regardless of the culture conditions or disease status. It also allowed us to differentiate between healthy and osteoarthritic cartilage. The clustering also revealed the relative importance of the investigated culturing conditions (stimulation agent, stimulation time, bead/monolayer). We augmented the cluster analysis with a statistical search for genes showing differential expression. The identified genes provided hints to the molecular basis of the differences between the sample classes. Our approach shows the power of modern bioinformatic algorithms for understanding and class ifying chondrocytic phenotypes in vivo and in vitro. Although it does not generate new experimental data per se, it provides valuable information regarding the biology of chondrocytes and may provide tools for diagnosing and staging the osteoarthritic disease process.

ei

DOI [BibTex]

ei Zien, A., Gebhard, P., Fundel, K., Aigner, T. Phenotyping of Chondrocytes In Vivo and In Vitro Using cDNA Array Technology Clinical Orthopaedics and Related Research, 460, pages: 226-233, July 2007 (article)

DOI [BibTex]

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Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana

Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthmann, N., Hu, T., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Rätsch, G., Ecker, J., Weigel, D.

Science, 317(5836):338-342, July 2007 (article)

Abstract
The genomes of individuals from the same species vary in sequence as a result of different evolutionary processes. To examine the patterns of, and the forces shaping, sequence variation in Arabidopsis thaliana, we performed high-density array resequencing of 20 diverse strains (accessions). More than 1 million nonredundant single-nucleotide polymorphisms (SNPs) were identified at moderate false discovery rates (FDRs), and ~4% of the genome was identified as being highly dissimilar or deleted relative to the reference genome sequence. Patterns of polymorphism are highly nonrandom among gene families, with genes mediating interaction with the biotic environment having exceptional polymorphism levels. At the chromosomal scale, regional variation in polymorphism was readily apparent. A scan for recent selective sweeps revealed several candidate regions, including a notable example in which almost all variation was removed in a 500-kilobase window. Analyzing the polymorphisms we describe in larger sets of accessions will enable a detailed understanding of forces shaping population-wide sequence variation in A. thaliana.

ei

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ei Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthmann, N., Hu, T., Fu, G., Hinds, D., Chen, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Rätsch, G., Ecker, J., Weigel, D. Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana Science, 317(5836):338-342, July 2007 (article)

PDF DOI [BibTex]

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Graph Laplacians and their Convergence on Random Neighborhood Graphs

Hein, M., Audibert, J., von Luxburg, U.

Journal of Machine Learning Research, 8, pages: 1325-1370, June 2007 (article)

Abstract
Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semi-supervised learning, dimensionality reduction and clustering. In this paper we determine the pointwise limit of three different graph Laplacians used in the literature as the sample size increases and the neighborhood size approaches zero. We show that for a uniform measure on the submanifold all graph Laplacians have the same limit up to constants. However in the case of a non-uniform measure on the submanifold only the so called random walk graph Laplacian converges to the weighted Laplace-Beltrami operator.

ei

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ei Hein, M., Audibert, J., von Luxburg, U. Graph Laplacians and their Convergence on Random Neighborhood Graphs Journal of Machine Learning Research, 8, pages: 1325-1370, June 2007 (article)

PDF PDF [BibTex]

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Asymptotic stability of the solution of the M/MB/1 queueing model

Haji, A., Radl, A.

Computers and Mathematics with Applications, 53(9):1411-1420, May 2007 (article)

ei

PDF DOI [BibTex]

ei Haji, A., Radl, A. Asymptotic stability of the solution of the M/MB/1 queueing model Computers and Mathematics with Applications, 53(9):1411-1420, May 2007 (article)

PDF DOI [BibTex]

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MR Angiography of Dural Arteriovenous Fistulas: Diagnosis and Follow-Up after Treatment Using a Time-Resolved 3D Contrast-Enhanced Technique

Meckel, S., Maier, M., San Millan Ruiz, D., Yilmaz, H., Scheffler, K., Radü, E., Wetzel, S.

American Journal of Neuroradiology, 28(5):877-884, May 2007 (article)

Abstract
BACKGROUND AND PURPOSE: Digital subtraction angiography (DSA) is the method of reference for imaging of dural arteriovenous fistula (DAVF). The goal of this study was to analyze the value of different MR images including 3D contrast-enhanced MR angiography (MRA) with a high temporal resolution in diagnostic and follow-up imaging of DAVFs. MATERIALS AND METHODS: A total of 18 MR/MRA examinations from 14 patients with untreated (n = 9) and/or treated (n = 9) DAVFs were evaluated. Two observers assessed all MR and MRA investigations for signs indicating the presence of a DAVF, for fistula characteristics such as fistula grading, location of fistulous point, and fistula obliteration after treatment. All results were compared with DSA findings. RESULTS: On time-resolved 3D contrast-enhanced (TR 3D) MRA, the side and presence of all patent fistulas (n = 13) were correctly indicated, and no false-positive findings were observed in occluded DAVFs (n = 5). Grading of fistulas with this imaging technique was correct in 77% and 85% of patent fistulas for both readers, respectively. On T2-weighted images, signs indicative of a DAVF were encountered only in fistulas with cortical venous reflux (56%), whereas on 3D time-of-flight (TOF) MRA, most fistulas (88%) were correctly detected. In complete fistula occlusion, false-positive findings were encountered on both T2-weighted images and on TOF MRA images. CONCLUSION: In this study, TR 3D MRA proved reliable in detecting DAVFs and suitable for follow-up imaging. The technique allowed—within limitations—to grade DAVFs. Although 3D TOF MRA can depict signs of DAVFs, its value for follow-up imaging is limited.

ei

Web [BibTex]

ei Meckel, S., Maier, M., San Millan Ruiz, D., Yilmaz, H., Scheffler, K., Radü, E., Wetzel, S. MR Angiography of Dural Arteriovenous Fistulas: Diagnosis and Follow-Up after Treatment Using a Time-Resolved 3D Contrast-Enhanced Technique American Journal of Neuroradiology, 28(5):877-884, May 2007 (article)

Web [BibTex]

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Bayesian Reconstruction of the Density of States

Habeck, M.

Physical Review Letters, 98(20, 200601):1-4, May 2007 (article)

Abstract
A Bayesian framework is developed to reconstruct the density of states from multiple canonical simulations. The framework encompasses the histogram reweighting method of Ferrenberg and Swendsen. The new approach applies to nonparametric as well as parametric models and does not require simulation data to be discretized. It offers a means to assess the precision of the reconstructed density of states and of derived thermodynamic quantities.

ei

Web DOI [BibTex]

ei Habeck, M. Bayesian Reconstruction of the Density of States Physical Review Letters, 98(20, 200601):1-4, May 2007 (article)

Web DOI [BibTex]

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PALMA: mRNA to Genome Alignments using Large Margin Algorithms

Schulze, U., Hepp, B., Ong, C., Rätsch, G.

Bioinformatics, 23(15):1892-1900, May 2007 (article)

Abstract
Motivation: Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. Results: We present a novel approach based on large margin learning that combines accurate plice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm – called PALMA – tunes the parameters of the model such that true alignments score higher than other alignments. We study the accuracy of alignments of mRNAs containing artificially generated micro-exons to genomic DNA. In a carefully designed experiment, we show that our algorithm accurately identifies the intron boundaries as well as boundaries of the optimal local alignment. It outperforms all other methods: for 5702 artificially shortened EST sequences from C. elegans and human it correctly identifies the intron boundaries in all except two cases. The best other method is a recently proposed method called exalin which misaligns 37 of the sequences. Our method also demonstrates robustness to mutations, insertions and deletions, retaining accuracy even at high noise levels. Availability: Datasets for training, evaluation and testing, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/palma.

ei

Web DOI [BibTex]

ei Schulze, U., Hepp, B., Ong, C., Rätsch, G. PALMA: mRNA to Genome Alignments using Large Margin Algorithms Bioinformatics, 23(15):1892-1900, May 2007 (article)

Web DOI [BibTex]

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The role of the striatum in adaptation learning: a computational model

Grosse-Wentrup, M., Contreras-Vidal, J.

Biological Cybernetics, 96(4):377-388, April 2007 (article)

Abstract
To investigate the functional role of the striatum in visuo-motor adaptation, we extend the DIRECT-model for visuo-motor reaching movements formulated by Bullock et al.(J Cogn Neurosci 5:408–435,1993) through two parallel loops, each modeling a distinct contribution of the cortico–cerebellar–thalamo–cortical and the cortico–striato–thalamo–cortical networks to visuo-motor adaptation. Based on evidence of Robertson and Miall(Neuroreport 10(5): 1029–1034, 1999), we implement the function of the cortico–cerebellar–thalamo–cortical loop as a module that gradually adapts to small changes in sensorimotor relationships. The cortico–striato–thalamo–cortical loop on the other hand is hypothesized to act as an adaptive search element, guessing new sensorimotor-transformations and reinforcing successful guesses while punishing unsuccessful ones. In a first step, we show that the model reproduces trajectories and error curves of healthy subjects in a two dimensional center-out reaching task with rotated screen cursor visual feedback. In a second step, we disable learning processes in the cortico–striato– thalamo–cortical loop to simulate subjects with Parkinson’s disease (PD), and show that this leads to error curves typical of subjects with PD. We conclude that the results support our hypothesis, i.e., that the role of the cortico–striato–thalamo–cortical loop in visuo-motor adaptation is that of an adaptive search element.

ei

PDF PDF DOI [BibTex]

ei Grosse-Wentrup, M., Contreras-Vidal, J. The role of the striatum in adaptation learning: a computational model Biological Cybernetics, 96(4):377-388, April 2007 (article)

PDF PDF DOI [BibTex]

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A robust fetal ECG detection method for abdominal recordings

Martens, SMM., Rabotti, C., Mischi, M., Sluijter, RJ.

Physiological Measurement, 28(4):373-388, April 2007, Martin Black Prize for best paper Physiological Measurement 2007 (article)

Abstract
In this paper, we propose a new method for FECG detection in abdominal recordings. The method consists of a sequential analysis approach, in which the a priori information about the interference signals is used for the detection of the FECG. Our method is evaluated on a set of 20 abdominal recordings from pregnant women with different gestational ages. Its performance in terms of fetal heart rate (FHR) detection success is compared with that of independent component analysis (ICA). The results show that our sequential estimation method outperforms ICA with a FHR detection rate of 85% versus 60% of ICA. The superior performance of our method is especially evident in recordings with a low signal-to-noise ratio (SNR). This indicates that our method is more robust than ICA for FECG detection.

ei

DOI [BibTex]

ei Martens, SMM., Rabotti, C., Mischi, M., Sluijter, RJ. A robust fetal ECG detection method for abdominal recordings Physiological Measurement, 28(4):373-388, April 2007, Martin Black Prize for best paper Physiological Measurement 2007 (article)

DOI [BibTex]

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Neighborhood Property based Pattern Selection for Support Vector Machines

Shin, H., Cho, S.

Neural Computation, 19(3):816-855, March 2007 (article)

Abstract
The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.

ei

PDF Web DOI [BibTex]

ei Shin, H., Cho, S. Neighborhood Property based Pattern Selection for Support Vector Machines Neural Computation, 19(3):816-855, March 2007 (article)

PDF Web DOI [BibTex]

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Training a Support Vector Machine in the Primal

Chapelle, O.

Neural Computation, 19(5):1155-1178, March 2007 (article)

Abstract
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and that there is no reason for ignoring this possibilty. On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.

ei

PDF Web DOI [BibTex]

ei Chapelle, O. Training a Support Vector Machine in the Primal Neural Computation, 19(5):1155-1178, March 2007 (article)

PDF Web DOI [BibTex]

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Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning

Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., Schölkopf, B.

PLoS Computational Biology, 3(2, e20):0313-0322, February 2007 (article)

ei

PDF DOI [BibTex]

ei Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., Schölkopf, B. Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning PLoS Computational Biology, 3(2, e20):0313-0322, February 2007 (article)

PDF DOI [BibTex]

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Statistical Consistency of Kernel Canonical Correlation Analysis

Fukumizu, K., Bach, F., Gretton, A.

Journal of Machine Learning Research, 8, pages: 361-383, February 2007 (article)

Abstract
While kernel canonical correlation analysis (CCA) has been applied in many contexts, the convergence of finite sample estimates of the associated functions to their population counterparts has not yet been established. This paper gives a mathematical proof of the statistical convergence of kernel CCA, providing a theoretical justification for the method. The proof uses covariance operators defined on reproducing kernel Hilbert spaces, and analyzes the convergence of their empirical estimates of finite rank to their population counterparts, which can have infinite rank. The result also gives a sufficient condition for convergence on the regularization coefficient involved in kernel CCA: this should decrease as n^{-1/3}, where n is the number of data.

ei

PDF [BibTex]

ei Fukumizu, K., Bach, F., Gretton, A. Statistical Consistency of Kernel Canonical Correlation Analysis Journal of Machine Learning Research, 8, pages: 361-383, February 2007 (article)

PDF [BibTex]

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Some observations on the pedestal effect

Henning, G., Wichmann, F.

Journal of Vision, 7(1:3):1-15, January 2007 (article)

Abstract
The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when it is added to a masking or pedestal grating of the same spatial frequency, orientation, and phase. We measured the pedestal effect in both broadband and notched noiseVnoise from which a 1.5-octave band centered on the signal frequency had been removed. Although the pedestal effect persists in broadband noise, it almost disappears in the notched noise. Furthermore, the pedestal effect is substantial when either high- or low-pass masking noise is used. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies different from that of the signal and the pedestal. We speculate that the spatial-frequency components of the notched noise above and below the spatial frequency of the signal and the pedestal prevent ‘‘off-frequency looking,’’ that is, prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and the pedestal. Thus, the pedestal or dipper effect measured without notched noise appears not to be a characteristic of individual spatial-frequency-tuned channels.

ei

PDF Web DOI [BibTex]

ei Henning, G., Wichmann, F. Some observations on the pedestal effect Journal of Vision, 7(1:3):1-15, January 2007 (article)

PDF Web DOI [BibTex]

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Cue Combination and the Effect of Horizontal Disparity and Perspective on Stereoacuity

Zalevski, AM., Henning, GB., Hill, NJ.

Spatial Vision, 20(1):107-138, January 2007 (article)

Abstract
Relative depth judgments of vertical lines based on horizontal disparity deteriorate enormously when the lines form part of closed configurations (Westheimer, 1979). In studies showing this effect, perspective was not manipulated and thus produced inconsistency between horizontal disparity and perspective. We show that stereoacuity improves dramatically when perspective and horizontal disparity are made consistent. Observers appear to use unhelpful perspective cues in judging the relative depth of the vertical sides of rectangles in a way not incompatible with a form of cue weighting. However, 95% confidence intervals for the weights derived for cues usually exceed the a-priori [0-1] range.

ei

PDF PDF DOI [BibTex]

ei Zalevski, AM., Henning, GB., Hill, NJ. Cue Combination and the Effect of Horizontal Disparity and Perspective on Stereoacuity Spatial Vision, 20(1):107-138, January 2007 (article)

PDF PDF DOI [BibTex]

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Classificazione di immagini telerilevate satellitari per agricoltura di precisione

Arnoldi, E., Bruzzone, L., Carlin, L., Pedron, L., Persello, C.

MondoGis: Il Mondo dei Sistemi Informativi Geografici, 63, pages: 13-17, 2007 (article)

ei

[BibTex]

ei Arnoldi, E., Bruzzone, L., Carlin, L., Pedron, L., Persello, C. Classificazione di immagini telerilevate satellitari per agricoltura di precisione MondoGis: Il Mondo dei Sistemi Informativi Geografici, 63, pages: 13-17, 2007 (article)

[BibTex]

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Separating convolutive mixtures by pairwise mutual information minimization", IEEE Signal Processing Letters

Zhang, K., Chan, L.

IEEE Signal Processing Letters, 14(12):992-995, 2007 (article)

Abstract
Blind separation of convolutive mixtures by minimizing the mutual information between output sequences can avoid the side effect of temporally whitening the outputs, but it involves the score function difference, whose estimation may be problematic when the data dimension is greater than two. This greatly limits the application of this method. Fortunately, for separating convolutive mixtures, pairwise independence of outputs leads to their mutual independence. As an implementation of this idea, we propose a way to separate convolutive mixtures by enforcing pairwise independence. This approach can be applied to separate convolutive mixtures of a moderate number of sources.

ei

Web [BibTex]

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The new robotics - towards human-centered machines

Schaal, S.

HFSP Journal Frontiers of Interdisciplinary Research in the Life Sciences, 1(2):115-126, 2007, clmc (article)

Abstract
Research in robotics has moved away from its primary focus on industrial applications. The New Robotics is a vision that has been developed in past years by our own university and many other national and international research instiutions and addresses how increasingly more human-like robots can live among us and take over tasks where our current society has shortcomings. Elder care, physical therapy, child education, search and rescue, and general assistance in daily life situations are some of the examples that will benefit from the New Robotics in the near future. With these goals in mind, research for the New Robotics has to embrace a broad interdisciplinary approach, ranging from traditional mathematical issues of robotics to novel issues in psychology, neuroscience, and ethics. This paper outlines some of the important research problems that will need to be resolved to make the New Robotics a reality.

am

link (url) [BibTex]

am Schaal, S. The new robotics - towards human-centered machines HFSP Journal Frontiers of Interdisciplinary Research in the Life Sciences, 1(2):115-126, 2007, clmc (article)

link (url) [BibTex]

2003

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Molecular phenotyping of human chondrocyte cell lines T/C-28a2, T/C-28a4, and C-28/I2

Finger, F., Schorle, C., Zien, A., Gebhard, P., Goldring, M., Aigner, T.

Arthritis & Rheumatism, 48(12):3395-3403, December 2003 (article)

ei

[BibTex]

2003

ei Finger, F., Schorle, C., Zien, A., Gebhard, P., Goldring, M., Aigner, T. Molecular phenotyping of human chondrocyte cell lines T/C-28a2, T/C-28a4, and C-28/I2 Arthritis & Rheumatism, 48(12):3395-3403, December 2003 (article)

[BibTex]

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A Study on Rainfall - Runoff Models for Improving Ensemble Streamflow Prediction: 1. Rainfallrunoff Models Using Artificial Neural Networks

Jeong, D., Kim, Y., Cho, S., Shin, H.

Journal of the Korean Society of Civil Engineers, 23(6B):521-530, December 2003 (article)

Abstract
The previous ESP (Ensemble Streamflow Prediction) studies conducted in Korea reported that the modeling error is a major source of the ESP forecast error in winter and spring (i.e. dry seasons), and thus suggested that improving the rainfall-runoff model would be critical to obtain more accurate probabilistic forecasts with ESP. This study used two types of Artificial Neural Networks (ANN), such as a Single Neural Network (SNN) and an Ensemble Neural Networks (ENN), to improve the simulation capability of the rainfall-runoff model of the ESP forecasting system for the monthly inflow to the Daecheong dam. Applied for the first time to Korean hydrology, ENN combines the outputs of member models so that it can control the generalization error better than SNN. Because the dry and the flood season in Korea shows considerably different streamflow characteristics, this study calibrated the rainfall-runoff model separately for each season. Therefore, four rainfall-runoff models were developed according to the ANN types and the seasons. This study compared the ANN models with a conceptual rainfall-runoff model called TANK and verified that the ANN models were superior to TANK. Among the ANN models, ENN was more accurate than SNN. The ANN model performance was improved when the model was calibrated separately for the dry and the flood season. The best ANN model developed in this article will be incorporated into the ESP system to increase the forecast capability of ESP for the monthly inflow to the Daecheong dam.

ei

[BibTex]

ei Jeong, D., Kim, Y., Cho, S., Shin, H. A Study on Rainfall - Runoff Models for Improving Ensemble Streamflow Prediction: 1. Rainfallrunoff Models Using Artificial Neural Networks Journal of the Korean Society of Civil Engineers, 23(6B):521-530, December 2003 (article)

[BibTex]

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Quantitative Cerebral Blood Flow Measurements in the Rat Using a Beta-Probe and H215O

Weber, B., Spaeth, N., Wyss, M., Wild, D., Burger, C., Stanley, R., Buck, A.

Journal of Cerebral Blood Flow and Metabolism, 23(12):1455-1460, December 2003 (article)

Abstract
Beta-probes are a relatively new tool for tracer kinetic studies in animals. They are highly suited to evaluate new positron emission tomography tracers or measure physiologic parameters at rest and after some kind of stimulation or intervention. In many of these experiments, the knowledge of CBF is highly important. Thus, the purpose of this study was to evaluate the method of CBF measurements using a beta-probe and H215O. CBF was measured in the barrel cortex of eight rats at baseline and after acetazolamide challenge. Trigeminal nerve stimulation was additionally performed in five animals. In each category, three injections of 250 to 300 MBq H215O were performed at 10-minute intervals. Data were analyzed using a standard one-tissue compartment model (K1 = CBF, k2 = CBF/p, where p is the partition coefficient). Values for K1 were 0.35 plusminus 0.09, 0.58 plusminus 0.16, and 0.49 plusminus 0.03 mL dot min-1 dot mL-1 at rest, after acetazolamide challenge, and during trigeminal nerve stimulation, respectively. The corresponding values for k2 were 0.55 plusminus 0.12, 0.94 plusminus 0.16, and 0.85 plusminus 0.12 min-7, and for p were 0.64 plusminus 0.05, 0.61 plusminus 0.07, and 0.59 plusminus 0.06.The standard deviation of the difference between two successive experiments, a measure for the reproducibility of the method, was 10.1%, 13.0%, and 5.7% for K1, k2, and p, respectively. In summary, beta-probes in conjunction with H215O allow the reproducible quantitative measurement of CBF, although some systematic underestimation seems to occur, probably because of partial volume effects.

ei

PDF DOI [BibTex]

ei Weber, B., Spaeth, N., Wyss, M., Wild, D., Burger, C., Stanley, R., Buck, A. Quantitative Cerebral Blood Flow Measurements in the Rat Using a Beta-Probe and H215O Journal of Cerebral Blood Flow and Metabolism, 23(12):1455-1460, December 2003 (article)

PDF DOI [BibTex]

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Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation

Ziehe, A., Kawanabe, M., Harmeling, S., Müller, K.

Journal of Machine Learning Research, 4(7-8):1319-1338, November 2003 (article)

Abstract
We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm--a powerful technique from non-parametric statistics--to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearities. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results.

ei

PDF PDF DOI [BibTex]

ei Ziehe, A., Kawanabe, M., Harmeling, S., Müller, K. Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation Journal of Machine Learning Research, 4(7-8):1319-1338, November 2003 (article)

PDF PDF DOI [BibTex]

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Correlated stage- and subfield-associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy

Becker, A., Chen, J., Zien, A., Sochivko, D., Normann, S., Schramm, J., Elger, C., Wiestler, O., Blumcke, I.

European Journal of Neuroscience, 18(10):2792-2802, November 2003 (article)

Abstract
Epileptic activity evokes profound alterations of hippocampal organization and function. Genomic responses may reflect immediate consequences of excitatory stimulation as well as sustained molecular processes related to neuronal plasticity and structural remodeling. Using oligonucleotide microarrays with 8799 sequences, we determined subregional gene expression profiles in rats subjected to pilocarpine-induced epilepsy (U34A arrays, Affymetrix, Santa Clara, CA, USA; P < 0.05, twofold change, n = 3 per stage). Patterns of gene expression corresponded to distinct stages of epilepsy development. The highest number of differentially expressed genes (dentate gyrus, approx. 400 genes and CA1, approx. 700 genes) was observed 3 days after status epilepticus. The majority of up-regulated genes was associated with mechanisms of cellular stress and injury - 14 days after status epilepticus, numerous transcription factors and genes linked to cytoskeletal and synaptic reorganization were differentially expressed and, in the stage of chronic spontaneous seizures, distinct changes were observed in the transcription of genes involved in various neurotransmission pathways and between animals with low vs. high seizure frequency. A number of genes (n = 18) differentially expressed during the chronic epileptic stage showed corresponding expression patterns in hippocampal subfields of patients with pharmacoresistant temporal lobe epilepsy (n = 5 temporal lobe epilepsy patients; U133A microarrays, Affymetrix; covering 22284 human sequences). These data provide novel insights into the molecular mechanisms of epileptogenesis and seizure-associated cellular and structural remodeling of the hippocampus.

ei

[BibTex]

ei Becker, A., Chen, J., Zien, A., Sochivko, D., Normann, S., Schramm, J., Elger, C., Wiestler, O., Blumcke, I. Correlated stage- and subfield-associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy European Journal of Neuroscience, 18(10):2792-2802, November 2003 (article)

[BibTex]

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Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

ei

PostScript [BibTex]

ei Bousquet, O. Concentration Inequalities for Sub-Additive Functions Using the Entropy Method Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

PostScript [BibTex]

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YKL-39 (chitinase 3-like protein 2), but not YKL-40 (chitinase 3-like protein 1), is up regulated in osteoarthritic chondrocytes

Knorr, T., Obermayr, F., Bartnik, E., Zien, A., Aigner, T.

Annals of the Rheumatic Diseases, 62(10):995-998, October 2003 (article)

Abstract
OBJECTIVE: To investigate quantitatively the mRNA expression levels of YKL-40, an established marker of rheumatoid and osteoarthritic cartilage degeneration in synovial fluid and serum, and a closely related molecule YKL-39, in articular chondrocytes. METHODS: cDNA array and online quantitative polymerase chain reaction (PCR) were used to measure mRNA expression levels of YKL-39 and YKL-40 in chondrocytes in normal, early degenerative, and late stage osteoarthritic cartilage samples. RESULTS: Expression analysis showed high levels of both proteins in normal articular chondrocytes, with lower levels of YKL-39 than YKL-40. Whereas YKL-40 was significantly down regulated in late stage osteoarthritic chondrocytes, YKL-39 was significantly up regulated. In vitro both YKLs were down regulated by interleukin 1beta. CONCLUSIONS: The up regulation of YKL-39 in osteoarthritic cartilage suggests that YKL-39 may be a more accurate marker of chondrocyte activation than YKL-40, although it has yet to be established as a suitable marker in synovial fluid and serum. The decreased expression of YKL-40 by osteoarthritic chondrocytes is surprising as increased levels have been reported in rheumatoid and osteoarthritic synovial fluid, where it may derive from activated synovial cells or osteophytic tissue or by increased matrix destruction in the osteoarthritic joint. YKL-39 and YKL-40 are potentially interesting marker molecules for arthritic joint disease because they are abundantly expressed by both normal and osteoarthritic chondrocytes.

ei

[BibTex]

ei Knorr, T., Obermayr, F., Bartnik, E., Zien, A., Aigner, T. YKL-39 (chitinase 3-like protein 2), but not YKL-40 (chitinase 3-like protein 1), is up regulated in osteoarthritic chondrocytes Annals of the Rheumatic Diseases, 62(10):995-998, October 2003 (article)

[BibTex]

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Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

ei

Web DOI [BibTex]

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Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

ei

PDF DOI [BibTex]

ei Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W. Dealing with large Diagonals in Kernel Matrices Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

PDF DOI [BibTex]

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The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

ei

PDF [BibTex]

ei Tsuda, K., Akaho, S., Asai, K. The em Algorithm for Kernel Matrix Completion with Auxiliary Data Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

PDF [BibTex]

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Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

ei

DOI [BibTex]

ei Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K. Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

DOI [BibTex]

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Kernel-based nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

Neural Computation, 15(5):1089-1124, May 2003 (article)

Abstract
We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibly infinite)—dimensional kernel feature space. In practice, however, the data form a smaller submanifold in feature space—even smaller than the number of training data points—a fact that has already been used by, for example, reduced set techniques for support vector machines. We propose to adapt to this effective dimension as a preprocessing step and to construct an orthonormal basis of this submanifold. The latter dimension-reduction step is essential for making the subsequent application of BSS methods computationally and numerically tractable. In the reduced space, we use a BSS algorithm that is based on second-order temporal decorrelation. Finally, we propose a selection procedure to obtain the original sources from the extracted nonlinear components automatically. Experiments demonstrate the excellent performance and efficiency of our kTDSEP algorithm for several problems of nonlinear BSS and for more than two sources.

ei

PDF Web DOI [BibTex]

ei Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K. Kernel-based nonlinear blind source separation Neural Computation, 15(5):1089-1124, May 2003 (article)

PDF Web DOI [BibTex]