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Prediction of Protein Function from Networks


Book Chapter


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.

Author(s): Shin, H. and Tsuda, K.
Book Title: Semi-Supervised Learning
Pages: 361-376
Year: 2006
Month: November
Day: 0

Series: Adaptive Computation and Machine Learning
Editors: Chapelle, O. , B. Sch{\"o}lkopf, A. Zien
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Prediction of Protein Function from Networks},
  author = {Shin, H. and Tsuda, K.},
  booktitle = {Semi-Supervised Learning},
  pages = {361-376},
  series = {Adaptive Computation and Machine Learning},
  editors = {Chapelle, O. , B. Sch{\"o}lkopf, A. Zien},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = nov,
  year = {2006},
  month_numeric = {11}