Assessing the limits of genomic data integration for predicting protein networks Export

@article{citeulike:334796, abstract = {10.1101/gr.3610305 Genomic data integration\^{a}€”the process of statistically combining diverse sources of information from functional genomics experiments to make large-scale predictions\^{a}€”is becoming increasingly prevalent. One might expect that this process should become progressively more powerful with the integration of more evidence. Here, we explore the limits of genomic data integration, assessing the degree to which predictive power increases with the addition of more features. We focus on a predictive context that has been extensively investigated and benchmarked in the past\^{a}€”the prediction of protein\^{a}€“protein interactions in yeast. We start by using a simple Naive Bayes classifier for integrating diverse sources of genomic evidence, ranging from coexpression relationships to similar phylogenetic profiles. We expand the number of features considered for prediction to 16, significantly more than previous studies. Overall, we observe a small, but measurable improvement in prediction performance over previous benchmarks, based on four strong features. This allows us to identify new yeast interactions with high confidence. It also allows us to quantitatively assess the inter-relations amongst different genomic features. It is known that subtle correlations and dependencies between features can confound the strength of interaction predictions. We investigate this issue in detail through calculating mutual information. To our surprise, we find no appreciable statistical dependence between the many possible pairs of features. We further explore feature dependencies by comparing the performance of our simple Naive Bayes classifier with a boosted version of the same classifier, which is fairly resistant to feature dependence. We find that boosting does not improve performance, indicating that, at least for prediction purposes, our genomic features are essentially independent. In summary, by integrating a few (i.e., four) good features, we approach the maximal predictive power of current genomic data integration; moreover, this limitation does not reflect (potentially removable) inter-relationships between the features.}, address = {Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA.}, author = {Lu, Long J. and Xia, Yu and Paccanaro, Alberto and Yu, Haiyuan and Gerstein, Mark}, citeulike-article-id = {334796}, citeulike-linkout-0 = {http://dx.doi.org/10.1101/gr.3610305}, citeulike-linkout-1 = {http://genome.cshlp.org/content/15/7/945.long.abstract}, citeulike-linkout-2 = {http://genome.cshlp.org/content/15/7/945.long.full.pdf}, citeulike-linkout-3 = {http://www.genome.org/cgi/content/abstract/15/7/945}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/15998909}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=15998909}, doi = {10.1101/gr.3610305}, issn = {1088-9051}, journal = {Genome Research}, keywords = {580-project, system-biology}, month = {July}, number = {7}, pages = {945--953}, posted-at = {2009-03-26 05:03:55}, priority = {2}, title = {Assessing the limits of genomic data integration for predicting protein networks}, url = {http://dx.doi.org/10.1101/gr.3610305}, volume = {15}, year = {2005} }

TY - JOUR ID - citeulike:334796 L3 - citeulike-article-id:334796 N2 - 10.1101/gr.3610305 Genomic data integration—the process of statistically combining diverse sources of information from functional genomics experiments to make large-scale predictions—is becoming increasingly prevalent. One might expect that this process should become progressively more powerful with the integration of more evidence. Here, we explore the limits of genomic data integration, assessing the degree to which predictive power increases with the addition of more features. We focus on a predictive context that has been extensively investigated and benchmarked in the past—the prediction of protein–protein interactions in yeast. We start by using a simple Naive Bayes classifier for integrating diverse sources of genomic evidence, ranging from coexpression relationships to similar phylogenetic profiles. We expand the number of features considered for prediction to 16, significantly more than previous studies. Overall, we observe a small, but measurable improvement in prediction performance over previous benchmarks, based on four strong features. This allows us to identify new yeast interactions with high confidence. It also allows us to quantitatively assess the inter-relations amongst different genomic features. It is known that subtle correlations and dependencies between features can confound the strength of interaction predictions. We investigate this issue in detail through calculating mutual information. To our surprise, we find no appreciable statistical dependence between the many possible pairs of features. We further explore feature dependencies by comparing the performance of our simple Naive Bayes classifier with a boosted version of the same classifier, which is fairly resistant to feature dependence. We find that boosting does not improve performance, indicating that, at least for prediction purposes, our genomic features are essentially independent. In summary, by integrating a few (i.e., four) good features, we approach the maximal predictive power of current genomic data integration; moreover, this limitation does not reflect (potentially removable) inter-relationships between the features. IS - 7 JF - Genome Research SN - 1088-9051 AD - Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA. EP - 953 TI - Assessing the limits of genomic data integration for predicting protein networks VL - 15 SP - 945 KW - 580-project KW - system-biology AU - Lu, Long AU - Xia, Yu AU - Paccanaro, Alberto AU - Yu, Haiyuan AU - Gerstein, Mark PY - 2005/07// UR - http://dx.doi.org/10.1101/gr.3610305 ER -