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Motif Prediction

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Motif Prediction

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Research Frontiers in Probabilistic Graphical Models in Motif Discovery

Application of hidden Markov models and Bayesian networks to probabilistically infer and represent sequence motifs from large-scale biological datasets.

Latent Variable Models in Sequence Motif Architecture

Hierarchical Bayesian Networks for Regulatory Element Discovery

Factor Graphs and Sparse Motif Decomposition

Temporal Dependencies in Dynamic Motif Patterns

Belief Propagation for Context-Dependent Sequence Recognition

Markov Random Fields in Protein-DNA Binding Specificity

Structured Prediction of Motif Boundaries and Composition

Message Passing Algorithms for Multi-Scale Genomic Patterns

Variational Inference in Non-Additive Motif Interactions

Graphical Model Uncertainty in Isoform-Specific Motif Discovery

All Motif Prediction PhD categories