Protein Structure and Function Fast protein classification with multiple networks

Motivation: Support vector machines (SVMs) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced. In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n3), and produces a dense matrix of size n × n. Results: We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similar to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method. Availability: Software and data will be available on request. Contact: shin@tuebingen.mpg.de

[1]  B. Schwikowski,et al.  A network of protein–protein interactions in yeast , 2000, Nature Biotechnology.

[2]  Shang-Hua Teng,et al.  Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems , 2003, STOC '04.

[3]  William Stafford Noble,et al.  Learning kernels from biological networks by maximizing entropy , 2004, ISMB/ECCB.

[4]  N Linial,et al.  ProtoMap: Automatic classification of protein sequences, a hierarchy of protein families, and local maps of the protein space , 1999, Proteins.

[5]  Michael I. Jordan,et al.  Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.

[6]  Nello Cristianini,et al.  Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.

[7]  Ting Chen,et al.  An Integrated Probabilistic Model for Functional Prediction of Proteins , 2004, J. Comput. Biol..

[8]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[9]  G. Sumara,et al.  A Probabilistic Functional Network of Yeast Genes , 2004 .

[10]  Jean-Philippe Vert,et al.  Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCA , 2002, NIPS.

[11]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[12]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[13]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[14]  Yaniv Ziv,et al.  Revealing modular organization in the yeast transcriptional network , 2002, Nature Genetics.

[15]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[16]  Michael Gribskov,et al.  Use of Receiver Operating Characteristic (ROC) Analysis to Evaluate Sequence Matching , 1996, Comput. Chem..

[17]  Mikhail Belkin,et al.  Using manifold structure for partially labelled classification , 2002, NIPS 2002.

[18]  T. Takagi,et al.  Assessment of prediction accuracy of protein function from protein–protein interaction data , 2001, Yeast.

[19]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[20]  N. Blackstone.  Essential Cell Biology: An Introduction to the Molecular Biology of the Cell.Bruce Alberts , Dennis Bray , Alexander Johnson , Julian Lewis , Martin Raff , Keith Roberts , Peter Walter , 1998 .

[21]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[22]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[23]  Nello Cristianini,et al.  A statistical framework for genomic data fusion , 2004, Bioinform..

[24]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[25]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[26]  Ting Chen,et al.  An integrated probabilistic model for functional prediction of proteins , 2003, RECOMB '03.

[27]  Risi Kondor,et al.  Diffusion kernels on graphs and other discrete structures , 2002, ICML 2002.

[28]  Alessandro Vespignani,et al.  Global protein function prediction from protein-protein interaction networks , 2003, Nature Biotechnology.