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Biological sequence analysis and information processing by artificial neural networks Søren Brunak Center for Biological Sequence Analysis Technical University of Denmark brunak@cbs.dtu.dk
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Biological neuron
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Diversity of interactions in a network enables complex calculations Similar in biological and artificial systems Excitatory (+) and inhibitory (-) relations between compute units
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Transfer of biological principles to neural network algorithms Non-linear relation between input and output Massively parallel information processing Data-driven construction of algorithms Ability to generalize to new data items
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Simplest non-trivial classification problem CNHSYYP, HIETRRA, NWQSADY, NQYSEPR, WHITRCA, DYHSANY,... Two categories: positives and negatives Data described by two features, e.g. charge, sidechain volume, molecular weight, number of atoms,...
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Features of phosphorylations sites PKG cGMP- dep.kinase PKC CaM-II Ca++/cal- modulin-dep. kinase cdc2 Cyclin- dep.kinase 2 CK-II Casein kinase 2
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Homotypical cerebral cortex – (from primate) - 6 layers
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negative positive Training and error reduction
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DEMO
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Transfer of biological principles to neural network algorithms Non-linear relation between input and output Massively parallel information processing Data-driven construction of algorithms
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Sparse encoding of amino acid sequence windows
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Sparse encoding of nucleotide sequence windows Nucleotides 4 letter alphabet Normally no need for a fifth letter ACGTAGGCAATCTCAGACGTTTATC 1000010000100001100000100010010010001000000101000001010010000010100001000010000100010001100000010100
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