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  Prediction of Protein Function from Networks

Shin, H., & Tsuda, K. (2006). Prediction of Protein Function from Networks. In O. Chapelle, B. Schölkopf, & A. Zien (Eds.), Semi-Supervised Learning (pp. 361-376). Cambridge, MA, USA: MIT Press.

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 Creators:
Shin, H1, 2, Author              
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In computational biology, it is common to represent domain knowledge using graphs. Frequently there exist multiple graphs for the same set of nodes, representing information from different sources, and no single graph is sufficient to predict class labels of unlabelled nodes reliably. One way to enhance reliability is to integrate multiple graphs, since individual graphs are partly independent and partly complementary to each other for prediction. In this chapter, we describe an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple graphs are formulated into one convex optimization problem. The graph-combining method is applied to functional class prediction of yeast proteins.When compared with individual graphs, the combined graph with optimized weights performs significantly better than any single graph.When compared with the semidefinite programming-based support vector machine (SDP/SVM), it shows comparable accuracy in a remarkably short time. Compared with a combined graph with equal-valued weights, our method could select important graphs without loss of accuracy, which implies the desirable property of integration with selectivity.

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 Dates: 2006
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3509
DOI: 10.7551/mitpress/9780262033589.003.0020
 Degree: -

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Title: Semi-Supervised Learning
Source Genre: Book
 Creator(s):
Chapelle, O1, Editor            
Schölkopf, B1, Editor            
Zien, A1, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: 508 Volume / Issue: - Sequence Number: 20 Start / End Page: 361 - 376 Identifier: ISBN: 0-262-03358-5