Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A tutorial on learning with Bayesian networks
Learning in graphical models
Learning Bayesian networks with local structure
Learning in graphical models
Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Finding motifs using random projections
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Finding Regulatory Elements Using Joint Likelihoods for Sequence and Expression Profile Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Statistical Method for Finding Transcription Factor Binding Sites
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Bayesian networks: a unification for discrete and Gaussian domains
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Physical network models and multi-source data integration
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Data perturbation for escaping local maxima in learning
Eighteenth national conference on Artificial intelligence
Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
ACM SIGKDD Explorations Newsletter
Clustering of diverse genomic data using information fusion
Proceedings of the 2004 ACM symposium on Applied computing
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
A discriminative model for identifying spatial cis-regulatory modules
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Probabilistic discovery of overlapping cellular processes and their regulation
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Identifying Conserved Discriminative Motifs
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Nucleosome occupancy information improves de novo motif discovery
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Probabilistic in silico prediction of protein-peptide interactions
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Computational molecular biology of genome expression and regulation
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Modeling the combinatorial functions of multiple transcription factors
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Hi-index | 0.00 |
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show that the learned model predicts expression from sequence, and that it identifies coherent co-regulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these co-regulated "modules" and the combinatorial regulation effects that govern their behavior.