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• W2034691490 abstract "We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5' (donor) and 3' (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models." @default.
• W2034691490 created "2016-06-24" @default.
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• W2034691490 date "2004-03-01" @default.
• W2034691490 modified "2023-10-09" @default.
• W2034691490 title "Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals" @default.
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• W2034691490 doi "https://doi.org/10.1089/1066527041410418" @default.
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