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Biomolecular interactions using mach...

Bock, Joel Robert.

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Biomolecular interactions using machine learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Biomolecular interactions using machine learning./
Author:	Bock, Joel Robert.
Description:	147 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2282.
Contained By:	Dissertation Abstracts International64-05B.
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3091349
ISBN:	0496390646

Biomolecular interactions using machine learning.
Bock, Joel Robert.

Biomolecular interactions using machine learning. - 147 p.

Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2282.

Thesis (Ph.D.)--University of California, San Diego, 2003.

This thesis explores the automatic prediction of biomolecular interactions using machine learning. The overriding philosophy motivating these investigations is to model the interactions between biomolecules (proteins and small-molecule ligands) using simple features to represent characteristics that are hypothesized to contribute to binding.

ISBN: 0496390646Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Biomolecular interactions using machine learning.
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035 $a (UnM)AAI3091349
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100 1 $a Bock, Joel Robert. $3 1925934
245 1 0 $a Biomolecular interactions using machine learning.
300 $a 147 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2282.
500 $a Chair: David A. Gough.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2003.
520 $a This thesis explores the automatic prediction of biomolecular interactions using machine learning. The overriding philosophy motivating these investigations is to model the interactions between biomolecules (proteins and small-molecule ligands) using simple features to represent characteristics that are hypothesized to contribute to binding.
520 $a For these investigations, I use "support vector" learning to build discrimination functions that separate input features into classes, resulting in a hypothesis as to whether or not (or how strongly) the biomolecules will interact. These discrimination functions are based on training data sets of known interactions.
520 $a Individual chapters of the thesis center on different investigations which predict protein-protein interactions in a multi-species database, within a single organism and across species. A final study focuses on the prediction of binding free energy between a receptor and ligand.
520 $a An important contribution made by this research is the demonstration that no explicit information about three-dimensional protein structure is necessary to make predictions of protein interactions. This implies that researchers may proceed directly from sequence to inference of protein function, as represented by the context of its interaction with other biomolecules.
590 $a School code: 0033.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Biophysics, General. $3 1019105
690 $a 0541
690 $a 0786
710 2 0 $a University of California, San Diego. $3 1018093
773 0 $t Dissertation Abstracts International $g 64-05B.
790 1 0 $a Gough, David A., $e advisor
790 $a 0033
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3091349