The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Prediction games and arcing algorithms
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Information-theoretic inference of large transcriptional regulatory networks
EURASIP Journal on Bioinformatics and Systems Biology
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Gene regulatory networks model dependencies between genes, and thus they potentially explain normal cell physiology, as well as pathological phenotypes. Because high-throughput technologies for measuring gene expression provide increasingly complete and accurate expression profiles, reverse-engineering of the gene regulatory interactions from observational data is an active field of research. In this study we propose a new approach to the inference of regulatory networks - we transform the problem into a set of independent binary classification tasks. We solve them using AdaBoost ensemble classifier, and use the structure of the discriminative models to discover the associations between transcription factors and regulated genes. Compared to the existing methods, our proposed approach shows higher prediction precision for the network inferred from the expression data of E. coli. However, the method does not make assumptions about the nature of the regulatory interactions, which promises a good accuracy for the expression profiles of the other species.