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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

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

Biological neuron

Diversity of interactions in a network enables complex calculations

Similar in biological and artificial systems Excitatory (+) and inhibitory (-) relations between compute units

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

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, ...

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

Homotypical cerebral cortex – (from primate) - 6 layers

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Fri08_talk1_Brunak_ver...
Author: 
Søren Brunak
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Center for Biological ...
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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
Tags: 
biolog | algorithm | sequenc | data | kinas | network | relat | neural
Created: 
4/6/2005 6:35:26 AM
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