1. 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

4. Biological neuron

6. Diversity of interactions in a network enables complex calculations Similar in biological and artificial systems Excitatory (+) and inhibitory (-) relations between compute units

8. 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

11. 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,...

12. 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

15. Homotypical cerebral cortex – (from primate) - 6 layers

21. negative positive Training and error reduction

22. DEMO

23. Transfer of biological principles to neural network algorithms Non-linear relation between input and output Massively parallel information processing Data-driven construction of algorithms

24. Sparse encoding of amino acid sequence windows

25. Sparse encoding of nucleotide sequence windows Nucleotides 4 letter alphabet Normally no need for a fifth letter ACGTAGGCAATCTCAGACGTTTATC 1000010000100001100000100010010010001000000101000001010010000010100001000010000100010001100000010100