Learning to predict protein-protein interactions from protein sequences. Export

@article{citeulike:903731, abstract = {In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.}, address = {Unit\'{e} de Biochimie et Biologie Mol\'{e}culaire des Insectes, Institut Pasteur, 75724 Paris Cedex 15, France. sgomez@pasteur.fr}, author = {Gomez, S. M. and Noble, W. S. and Rzhetsky, A.}, citeulike-article-id = {903731}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btg352}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/19/15/1875}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/14555619}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=14555619}, day = {12}, doi = {10.1093/bioinformatics/btg352}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {interactome}, month = {October}, number = {15}, pages = {1875--1881}, posted-at = {2006-10-18 15:21:14}, priority = {2}, title = {Learning to predict protein-protein interactions from protein sequences.}, url = {http://dx.doi.org/10.1093/bioinformatics/btg352}, volume = {19}, year = {2003} }

TY - JOUR ID - citeulike:903731 L3 - citeulike-article-id:903731 N2 - In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques. IS - 15 JF - Bioinformatics SN - 1367-4803 AD - Unité de Biochimie et Biologie Moléculaire des Insectes, Institut Pasteur, 75724 Paris Cedex 15, France. sgomez@pasteur.fr EP - 1881 TI - Learning to predict protein-protein interactions from protein sequences. VL - 19 SP - 1875 KW - interactome AU - Gomez, SM AU - Noble, WS AU - Rzhetsky, A PY - 2003/10/12/ UR - http://dx.doi.org/10.1093/bioinformatics/btg352 ER -