ACM Computing Surveys (CSUR)
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Probabilistic discovery of overlapping cellular processes and their regulation
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The Journal of Machine Learning Research
Fuzzy Measures on the Gene Ontology for Gene Product Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Measuring semantic similarity between Gene Ontology terms
Data & Knowledge Engineering
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
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Gene regulatory networks have been long studied in model organisms as a means of identifying functional relationships among genes or their corresponding products. Despite many existing methods for genome-wide construction of such networks, solutions to the gene regulatory networks problem are however not trivial. Here, we present, a hybrid approach with gene expression profiles and gene ontology (HAEO). HAEO makes use of multimethods (overlapping clustering and reverse engineering methods) to effectively and efficiently construct gene regulatory networks from multisources (gene expression profiles and gene ontology). Application to yeast cell cycle dataset demonstrates HAEO's ability to construct validated gene regulatory networks, such as some potential gene regulatory pairs, which cannot be discovered by general inferring methods and identifying cycles (i.e., feedback loops) between genes. We also experimentally study the efficiency of building networks and show that the proposed method, HAEO is much faster than Bayesian networks method.