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Automating recognition of regions of...

Dalhousie University (Canada).

Automating recognition of regions of intrinsically poor multiple alignment using machine learning.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Automating recognition of regions of intrinsically poor multiple alignment using machine learning./
作者:	Shan, Yunfeng (Henry).
面頁冊數:	105 p.
附註:	Source: Masters Abstracts International, Volume: 42-03, page: 0974.
Contained By:	Masters Abstracts International42-03.
標題:	Computer Science. -
電子資源:	Download fulltext (下載全文)
ISBN:	0612836886

Automating recognition of regions of intrinsically poor multiple alignment using machine learning.
Shan, Yunfeng (Henry).

Automating recognition of regions of intrinsically poor multiple alignment using machine learning. - 105 p.

Source: Masters Abstracts International, Volume: 42-03, page: 0974.

Thesis (M.C.Sc.)--Dalhousie University (Canada), 2003.

Phylogenetic analysis requires alignment of gene sequences. Automatic alignment programs produce regions of intrinsically poor alignment that are currently detected and deleted manually. We present the results of a machine learning approach to the detection of these regions of an alignment. We compare three approaches: naive Bayes (NB), standard decision trees (C4.5), and support vector machine (SVM).

ISBN: 0612836886Subjects--Topical Terms:

1000005419
Computer Science.

Automating recognition of regions of intrinsically poor multiple alignment using machine learning.
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245 1 0 $a Automating recognition of regions of intrinsically poor multiple alignment using machine learning.
300 $a 105 p.
500 $a Source: Masters Abstracts International, Volume: 42-03, page: 0974.
500 $a Advisers: E. Milios; A. Roger.
502 $a Thesis (M.C.Sc.)--Dalhousie University (Canada), 2003.
520 $a Phylogenetic analysis requires alignment of gene sequences. Automatic alignment programs produce regions of intrinsically poor alignment that are currently detected and deleted manually. We present the results of a machine learning approach to the detection of these regions of an alignment. We compare three approaches: naive Bayes (NB), standard decision trees (C4.5), and support vector machine (SVM).
520 $a The results show that all three algorithms can accurately identify the bad sites of multiple sequence alignment based on three attributes which include the gap ratio, the normalized site log likelihood ratio and the degree of homoplasy (consistency index). The F-measure (product of precision and recall) reaches 0.970. Of the three algorithms, SVM provides the best performance for prediction of bad sites or ambiguous sites, but C4.5 provides the best performance for prediction of good sites. Of the three site classes, it is the most difficult to distinguish the ambiguous sites, but easiest to distinguish the bad sites. The F-measure is only 0.776 for prediction of ambiguous sites and 0.888 for prediction of good sites. No significant difference among three parsimony scores of gaps as attributes for reducing classification error was observed. The best performance for prediction of bad sites occurred when three kinds of the classifiers were trained on a balanced class distribution. The classifiers of C4.5 and NB trained on a balanced class distribution generally had the best performance for prediction of ambiguous sites. However, the random subset was better for SVM for prediction of ambiguous sites or good sites than the balanced subset. The classifiers of C4.5 and NB trained on the whole dataset had the best performance for the prediction of good sites.
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