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Graph kernels and applications in bi...

Alvarez Vega, Marco.

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Graph kernels and applications in bioinformatics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Graph kernels and applications in bioinformatics./
Author:	Alvarez Vega, Marco.
Description:	110 p.
Notes:	Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: 3699.
Contained By:	Dissertation Abstracts International73-06B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3495239
ISBN:	9781267184672

Graph kernels and applications in bioinformatics.
Alvarez Vega, Marco.

Graph kernels and applications in bioinformatics. - 110 p.

Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: 3699.

Thesis (Ph.D.)--Utah State University, 2012.

In recent years, machine learning has emerged as an important discipline. However, despite the popularity of machine learning techniques, data in the form of discrete structures are not fully exploited. For example, when data appear as graphs, the common choice is the transformation of such structures into feature vectors. This procedure, though convenient, does not always effectively capture topological relationships inherent to the data; therefore, the power of the learning process may be insufficient. In this context, the use of kernel functions for graphs arises as an attractive way to deal with such structured objects.

ISBN: 9781267184672Subjects--Topical Terms:

626642
Computer Science.

Graph kernels and applications in bioinformatics.
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020 $a 9781267184672
035 $a (MiAaPQ)AAI3495239
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100 1 $a Alvarez Vega, Marco. $3 2105421
245 1 0 $a Graph kernels and applications in bioinformatics.
300 $a 110 p.
500 $a Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: 3699.
500 $a Advisers: Xiaojun Qi; Changhui Yan.
502 $a Thesis (Ph.D.)--Utah State University, 2012.
520 $a In recent years, machine learning has emerged as an important discipline. However, despite the popularity of machine learning techniques, data in the form of discrete structures are not fully exploited. For example, when data appear as graphs, the common choice is the transformation of such structures into feature vectors. This procedure, though convenient, does not always effectively capture topological relationships inherent to the data; therefore, the power of the learning process may be insufficient. In this context, the use of kernel functions for graphs arises as an attractive way to deal with such structured objects.
520 $a On the other hand, several entities in computational biology applications, such as gene products or proteins, may be naturally represented by graphs. Hence, the demanding need for algorithms that can deal with structured data poses the question of whether the use of kernels for graphs can outperform existing methods to solve specific computational biology problems. In this dissertation, we address the challenges involved in solving two specific problems in computational biology, in which the data are represented by graphs.
520 $a First, we propose a novel approach for protein function prediction by modeling proteins as graphs. For each of the vertices in a protein graph, we propose the calculation of evolutionary profiles, which are derived from multiple sequence alignments from the amino acid residues within each vertex. We then use a shortest path graph kernel in conjunction with a support vector machine to predict protein function. We evaluate our approach under two instances of protein function prediction, namely, the discrimination of proteins as enzymes, and the recognition of DNA binding proteins. In both cases, our proposed approach achieves better prediction performance than existing methods.
520 $a Second, we propose two novel semantic similarity measures for proteins based on the gene ontology. The first measure directly works on the gene ontology by combining the pairwise semantic similarity scores between sets of annotating terms for a pair of input proteins. The second measure estimates protein semantic similarity using a shortest path graph kernel to take advantage of the rich semantic knowledge contained within ontologies. Our comparison with other methods shows that our proposed semantic similarity measures are highly competitive and the latter one outperforms state-of-the-art methods. Furthermore, our two methods are intrinsic to the gene ontology, in the sense that they do not rely on external sources to calculate similarities.
590 $a School code: 0241.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0984
690 $a 0715
710 2 $a Utah State University. $b Computer Science. $3 1032121
773 0 $t Dissertation Abstracts International $g 73-06B.
790 $a 0241
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3495239