Analysis of protein-protein interaction networks using random walks
Proceedings of the 5th international workshop on Bioinformatics
Kernelizing the output of tree-based methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Testing the significance of the RV coefficient
Computational Statistics & Data Analysis
Gene function prediction with gene interaction networks: a context graph kernel approach
IEEE Transactions on Information Technology in Biomedicine
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Conditional ranking on relational data
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Finding an optimal DE model for biological network inference
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
From biological to social networks: Link prediction based on multi-way spectral clustering
Data & Knowledge Engineering
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Motivation: An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. Results: This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. Availability: Softwares are available upon request.