Innovative computational methods for transcriptomic data analysis
Proceedings of the 2006 ACM symposium on Applied computing
Exploring gene causal interactions using an enhanced constraint-based method
Pattern Recognition
The Journal of Machine Learning Research
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Similarity of transcription profiles for genes in gene sets
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Describing the orthology signal in a PPI network at a functional, complex level
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Comparative analysis of classification methods for protein interaction verification system
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
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Motivation: Function annotation of an unclassified protein on the basis of its interaction partners is well documented in the literature. Reliable predictions of interactions from other data sources such as gene expression measurements would provide a useful route to function annotation. We investigate the global relationship of protein--protein interactions with gene expression. This relationship is studied in four evolutionarily diverse species, for which substantial information regarding their interactions and expression is available: human, mouse, yeast and Escherichia coli. Results: In E.coli the expression of interacting pairs is highly correlated in comparison to random pairs, while in the other three species, the correlation of expression of interacting pairs is only slightly stronger than that of random pairs. To strengthen the correlation, we developed a protocol to integrate ortholog information into the interaction and expression datasets. In all four genomes, the likelihood of predicting protein interactions from highly correlated expression data is increased using our protocol. In yeast, for example, the likelihood of predicting a true interaction, when the correlation is 0.9, increases from 1.4 to 9.4. The improvement demonstrates that protein interactions are reflected in gene expression and the correlation between the two is strengthened by evolution information. The results establish that co-expression of interacting protein pairs is more conserved than that of random ones. Availability: Complete lists of metagenes across the genomes, microarray and protein interaction dataset used in this study are available on our webpage: http://proteomics.bioengr.uic.edu/inter_expr/index.htm Contact: huilu@uic.edu