dorsal/arxiv
View SchemaKernel methods in genomics and computational biology
| Authors | Jean-Philippe Vert |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0510032 |
| URL | https://arxiv.org/abs/q-bio/0510032 |
Abstract
Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future.
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"abstract": "Support vector machines and kernel methods are increasingly popular in\ngenomics and computational biology, due to their good performance in real-world\napplications and strong modularity that makes them suitable to a wide range of\nproblems, from the classification of tumors to the automatic annotation of\nproteins. Their ability to work in high dimension, to process non-vectorial\ndata, and the natural framework they provide to integrate heterogeneous data\nare particularly relevant to various problems arising in computational biology.\nIn this chapter we survey some of the most prominent applications published so\nfar, highlighting the particular developments in kernel methods triggered by\nproblems in biology, and mention a few promising research directions likely to\nexpand in the future.",
"arxiv_id": "q-bio/0510032",
"authors": [
"Jean-Philippe Vert"
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"q-bio.QM"
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"title": "Kernel methods in genomics and computational biology",
"url": "https://arxiv.org/abs/q-bio/0510032"
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