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Machine Learning and Applications in Biology

2007

Conference Paper

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The emergence of the fields of computational biology and bioinformatics has alleviated the burden of solving many biological problems, saving the time and cost required for experiments and also providing predictions that guide new experiments. Within computational biology, machine learning algorithms have played a central role in dealing with the flood of biological data. The goal of this tutorial is to raise awareness and comprehension of machine learning so that biologists can properly match the task at hand to the corresponding analytical approach. We start by categorizing biological problem settings and introduce the general machine learning schemes that fit best to each or these categories. We then explore representative models in further detail, from traditional statistical models to recent kernel models, presenting several up-to-date research projects in bioinfomatics to exemplify how biological questions can benefit from a machine learning approach. Finally, we discuss how cooperation between biologis ts and machine learners might be made smoother.

Author(s): Shin, H.
Book Title: BioKorea 2007
Journal: Proceedings of BioKorea 2007
Pages: 337-366
Year: 2007
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: BioKorea 2007
Event Place: Seoul, Korea

Digital: 0
Institution: Korea Health Industry Development Institute (KHIDI)
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex

@inproceedings{4989,
  title = {Machine Learning and Applications in Biology},
  author = {Shin, H.},
  journal = {Proceedings of BioKorea 2007},
  booktitle = {BioKorea 2007},
  pages = {337-366},
  organization = {Max-Planck-Gesellschaft},
  institution = {Korea Health Industry Development Institute (KHIDI)},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2007},
  month_numeric = {9}
}