Learning generative models for protein fold families
نویسندگان
چکیده
منابع مشابه
Learning generative models for protein fold families.
We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistic...
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Statistical models of the amino acid composition of the proteins within a fold family are widely used in science and engineering. Existing techniques for learning probabilistic graphical models from multiple sequence alignments either make strong assumptions about the conditional independencies within the model (e.g., HMMs), or else use sub-optimal algorithms to learn the structure and paramete...
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ژورنال
عنوان ژورنال: Proteins: Structure, Function, and Bioinformatics
سال: 2011
ISSN: 0887-3585
DOI: 10.1002/prot.22934