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Using artificial neural networks to ...

Lee, Chi-Chiang.

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Using artificial neural networks to estimate evolutionary parameters.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Using artificial neural networks to estimate evolutionary parameters./
Author:	Lee, Chi-Chiang.
Description:	33 p.
Notes:	Source: Masters Abstracts International, Volume: 48-05, page: 2823.
Contained By:	Masters Abstracts International48-05.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1476169
ISBN:	9781109772302

Using artificial neural networks to estimate evolutionary parameters.
Lee, Chi-Chiang.

Using artificial neural networks to estimate evolutionary parameters. - 33 p.

Source: Masters Abstracts International, Volume: 48-05, page: 2823.

Thesis (M.S.)--University of Southern California, 2010.

The rapid growth in the amount of molecular genetic data being collected will, in many cases, require the development of new analytic methods for the analysis of that data. In this thesis, we explore the feasibility of using machine learning algorithms, in particular artificial neural networks, to estimate two evolutionary parameters of great interest: mutation and recombination rates. We show that this is possible, and that the performance of such methods depends crucially upon the existence of good summary statistics appropriate for the given parameter, as well as the format in which the data itself is represented.

ISBN: 9781109772302Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Using artificial neural networks to estimate evolutionary parameters.
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008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781109772302
035 $a (UMI)AAI1476169
035 $a AAI1476169
040 $a UMI $c UMI
100 1 $a Lee, Chi-Chiang. $3 1680837
245 1 0 $a Using artificial neural networks to estimate evolutionary parameters.
300 $a 33 p.
500 $a Source: Masters Abstracts International, Volume: 48-05, page: 2823.
500 $a Advisers: Stanley Azen; Paul Marjoram.
502 $a Thesis (M.S.)--University of Southern California, 2010.
520 $a The rapid growth in the amount of molecular genetic data being collected will, in many cases, require the development of new analytic methods for the analysis of that data. In this thesis, we explore the feasibility of using machine learning algorithms, in particular artificial neural networks, to estimate two evolutionary parameters of great interest: mutation and recombination rates. We show that this is possible, and that the performance of such methods depends crucially upon the existence of good summary statistics appropriate for the given parameter, as well as the format in which the data itself is represented.
590 $a School code: 0208.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
690 $a 0308
690 $a 0715
690 $a 0800
690 $a 0984
710 2 $a University of Southern California. $b Biostatistics. $3 1017862
773 0 $t Masters Abstracts International $g 48-05.
790 1 0 $a Azen, Stanley, $e advisor
790 1 0 $a Marjoram, Paul, $e advisor
790 1 0 $a Siegmund, Kimberly $e committee member
790 $a 0208
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1476169