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Bioinformatics The application of computer science to biological data

Bioinformatics The application of computer science to biological data. Tony C Smith Department of Computer Science University of Waikato tcs@cs.waikato.ac.nz. The essence is prediction …. My dog is very littl _ ?

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Bioinformatics The application of computer science to biological data

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  1. BioinformaticsThe application of computer science to biological data Tony C Smith Department of Computer Science University of Waikato tcs@cs.waikato.ac.nz

  2. The essence is prediction … My dog is very littl_ ? • We know that letters do not occur in English at random (e.g. ‘t’ is more common than ‘x’) • We know that context changes the probability of a letter (e.g. ‘x’ is more common than ‘t’ after the sequence “I eat Weet-Bi_”) Predicting symbols is fundamental to a wide range of important applications (e.g. encryption, compression) Bioinformatics Tony C Smith

  3. Prediction in bioinformatics • Predicting the location of genes in DNA • Predicting gene roles in an organism • Predicting errors in a genetic transcription • Predicting the function of proteins • Predicting diseases from molecular samples • Anything that involves “making a judgment”; a yes/no decision about whether some sample datum ‘does’ or ‘does not’ have some property. Bioinformatics Tony C Smith

  4. Representation W e e t – B i x 0101011101100101011001010111010000101101 … … to the computer, everything is binary! Bioinformatics Tony C Smith

  5. 0101011101100101011001010111010000101101 0101101100100111111011010011010000101101 A A C G T C A T T C G A T G A T T C G A Just as we can teach a computer to predict things about a sequence of letters in English prose, we can also teach it to predict things about a other sequences—like a genetic sequence Bioinformatics Tony C Smith

  6. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagc Bioinformatics Tony C Smith

  7. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactc Bioinformatics Tony C Smith

  8. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactcttgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagctgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgcaatcggcgctacgcttcaaaatttattatattcccggcgcggctacgttcatcccagcagcagcgattttaaaattaacgcatcagactctcgtcgcgttcgtcgcctttattcacgctaatggacgacatcttttactacgacggcgcctacgcatcgcagcatacgacgcccagcatagtattttagaggcgaggacatcatcatatcgcagctacagcgcatcagacgcatacgacgacgactacgacgacactaacgacgatgttgcgcacccacaccagttatatagagacgaactcgcatcagtgttgcgcacccacaccagttatatagagacgaactc Bioinformatics Tony C Smith

  9. A genetic prediction problem • A gene encodes a protein • It is a blueprint that provides biochemical instructions on how to construct a sequence of amino acids so as to make a working protein that will perform some function in the organism Bioinformatics Tony C Smith

  10. RNA RNA RNA RNA RNA transcription factor A genetic prediction problem untranslated region encoding region Bioinformatics Tony C Smith

  11. A genetic prediction problem untranslated region Bioinformatics Tony C Smith

  12. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc untranslated region Bioinformatics Tony C Smith

  13. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc What transcription factors bind to this gene? Where is the transcription factor binding site? Bioinformatics Tony C Smith

  14. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc Clues: A binding site is often a short general pattern E.g. CCGATNATCGG Bioinformatics Tony C Smith

  15. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc Clues: The patterns are often reverse complements E.g. CCGATNATCGG GGCTANTAGCC Bioinformatics Tony C Smith

  16. A genetic prediction problem ttgcaatcggcgctacgcttcaaaatttattatattcccggc Clues: Where there is one binding site, often there is another nearby. Bioinformatics Tony C Smith

  17. A genetic prediction problem All of these properties are the kinds of things for which computer science has developed algorithms and data structures to identify quickly and efficiently, and therefore it is exactly the kind of problem computer scientists should be able to solve. Bioinformatics Tony C Smith

  18. proteomics Three consecutive nucleotides in the coding region form a ‘codon’ … i.e. encode an amino acid. A string of amino acids makes a protein. 3 nucleotides, 4 possibilities each: 43 = 64 possible codons But there are only 20 amino acids! Bioinformatics Tony C Smith

  19. proteomics There is quite a bit of redundancy in codons. Glycine: GGA, GGC, GGG, GGT Tyrosine: TAT, TAC Methionine: ATG Bioinformatics Tony C Smith

  20. Amino Acid R group Amide group Carboxyl group Bioinformatics Tony C Smith

  21. Amino Acid tyrosine glycine Bioinformatics Tony C Smith

  22. Bioinformatics Tony C Smith

  23. Bioinformatics Tony C Smith

  24. Signal peptide • A relatively short sequence of amino residues at the N-terminus of the nascent protein typically 15-50 residues MAGPRPSPWARLLLAALISVSLSGTLARCKKAPVSKKCETCVGQAALTGL … • Cleaved off as protein passes through membrane (operates like a pass key) • Knowing signal peptide helps determine protein function in the organism Bioinformatics Tony C Smith

  25. Local biases in residues around the cleavage site Sequence regularities can be exploited by statistical and pattern-based models Bioinformatics Tony C Smith

  26. Existing solutions • Partial alignments (Altschul & Gish, 1996) • Neural networks (Nielsen at al., 1997) • Hidden Markov models (Nielsen et al., 1999) • Polypeptide probabilities (Chou, 2001) • Maximum entropy (Clote, 2002) Bioinformatics Tony C Smith

  27. SignalP (Nielsen et al., 1997-2004) HMMs (or NNs) used to predict cleavage point Bioinformatics Tony C Smith

  28. Existing methods all perform reasonably well and with about the same accuracy (90% eukaryotes, 87% gram-, 85% gram+) • Do not offer a transparent explanatory framework as to the underlying biology Many other learning algorithms do! (WEKA data mining tools, Waikato University) Bioinformatics Tony C Smith

  29. From sequences to text • Primary sequence data has many similarities with text • Amino residues (letters) • Polypeptides (words) • Secondary structures (phrases/sentences) Bioinformatics Tony C Smith

  30. Bioinformatics Tony C Smith

  31. From sequences to text • Primary sequence data looks like text • Amino residues (letters) • Polypeptides (words) • Secondary structures (phrases/sentences) • Tertiary structure (whole documents) • Approach: transform a sequence into a set of pseudo-text documents Bioinformatics Tony C Smith

  32. Approach • Problem is stated as two-class: an amino acid is either the first residue of the mature protein or it is not • Each residue is described by a single document, which includes as many electrochemical, structural or contextual facts as are available (desirable) Bioinformatics Tony C Smith

  33. Properties of amino acids Bioinformatics Tony C Smith

  34. Free facts about amino acids Bioinformatics Tony C Smith

  35. Residue as a document E.g. Cysteine Cys C aliphatic [yes], aromatic [no], hydrophobic [yes], charge [-], polarized [yes], small [no], number of nitrogen atoms [1], contains sulphur [yes], has a carbon ring [no], ionized [yes], valence [2], cbeta [no], covalent [yes], h-bond [yes], etc. (whatever else experimenter wants to include) Bioinformatics Tony C Smith

  36. Sample document PRNUM:1. AANUM:21. AMINO[-8]:L. ALIPH[-8]:-. AROMA[-8]:-. CBETA[-8]:-. CHARG[-8]:-. COVAL[-8]:-. HBOND[-8]:-. HPHOB[-8]:+. IONIZ[-8]:-. NITRO[-8]:1. POLAR[-8]:-. POSNG[-8]:0. SMALL[-8]:-. SULPH[-8]:-. TEENY[-8]:-. CRING[-8]:-. VALEN[-8]:2. AMINO[-7]:L. ALIPH[-7]:-. AROMA[-7]:-. CBETA[-7]:-. CHARG[-7]:-. COVAL[-7]:-. HBOND[-7]:-. HPHOB[-7]:+. IONIZ[-7]:-. NITRO[-7]:1. POLAR[-7]:-. POSNG[-7]:0. SMALL[-7]:-. SULPH[-7]:-. TEENY[-7]:-. CRING[-7]:-. VALEN[-7]:2. AMINO[-6]:F. ALIPH[-6]:+. AROMA[-6]:+. CBETA[-6]:-. CHARG[-6]:-. COVAL[-6]:-. HBOND[-6]:-. HPHOB[-6]:+. IONIZ[-6]:-. NITRO[-6]:1. POLAR[-6]:-. POSNG[-6]:0. SMALL[-6]:-. SULPH[-6]:-. TEENY[-6]:-. CRING[-6]:+. VALEN[-6]:2. AMINO[-5]:A. ALIPH[-5]:-. AROMA[-5]:-. CBETA[-5]:-. CHARG[-5]:-. COVAL[-5]:-. HBOND[-5]:-. HPHOB[-5]:-. IONIZ[-5]:-. NITRO[-5]:1. POLAR[-5]:-. POSNG[-5]:0. SMALL[-5]:+. SULPH[-5]:-. TEENY[-5]:+. CRING[-5]:-. VALEN[-5]:2. AMINO[-4]:T. ALIPH[-4]:+. AROMA[-4]:-. CBETA[-4]:+. CHARG[-4]:-. COVAL[-4]:-. HBOND[-4]:+. HPHOB[-4]:-. IONIZ[-4]:-. NITRO[-4]:1. POLAR[-4]:+. POSNG[-4]:0. SMALL[-4]:+. SULPH[-4]:-. TEENY[-4]:-. CRING[-4]:-. VALEN[-4]:2. AMINO[-3]:C. ALIPH[-3]:+. AROMA[-3]:-. CBETA[-3]:-. CHARG[-3]:-. COVAL[-3]:+. HBOND[-3]:+. HPHOB[-3]:+. IONIZ[-3]:+. NITRO[-3]:1. POLAR[-3]:+. POSNG[-3]:-. SMALL[-3]:-. SULPH[-3]:+. TEENY[-3]:-. CRING[-3]:-. VALEN[-3]:2. AMINO[-2]:I. ALIPH[-2]:-. AROMA[-2]:-. CBETA[-2]:+. CHARG[-2]:-. COVAL[-2]:-. HBOND[-2]:-. HPHOB[-2]:+. IONIZ[-2]:-. NITRO[-2]:1. POLAR[-2]:-. POSNG[-2]:0. SMALL[-2]:-. SULPH[-2]:-. TEENY[-2]:-. CRING[-2]:-. VALEN[-2]:2. AMINO[-1]:A. ALIPH[-1]:-. AROMA[-1]:-. CBETA[-1]:-. CHARG[-1]:-. COVAL[-1]:-. HBOND[-1]:-. HPHOB[-1]:-. IONIZ[-1]:-. NITRO[-1]:1. POLAR[-1]:-. POSNG[-1]:0. SMALL[-1]:+. SULPH[-1]:-. TEENY[-1]:+. CRING[-1]:-. VALEN[-1]:2. AMINO[0]:R. ALIPH[0]:+. AROMA[0]:-. CBETA[0]:-. CHARG[0]:+. COVAL[0]:-. HBOND[0]:+. HPHOB[0]:-. IONIZ[0]:+. NITRO[0]:4. POLAR[0]:+. POSNG[0]:+. SMALL[0]:-. SULPH[0]:-. TEENY[0]:-. CRING[0]:-. VALEN[0]:3. AMINO[1]:H. ALIPH[1]:+. AROMA[1]:+. CBETA[1]:-. CHARG[1]:+. COVAL[1]:-. HBOND[1]:+. HPHOB[1]:-. IONIZ[1]:+. NITRO[1]:3. POLAR[1]:+. POSNG[1]:+. SMALL[1]:-. SULPH[1]:-. TEENY[1]:-. CRING[1]:+. VALEN[1]:3. AMINO[2]:Q. ALIPH[2]:+. AROMA[2]:-. CBETA[2]:-. CHARG[2]:-. COVAL[2]:-. HBOND[2]:+. HPHOB[2]:-. IONIZ[2]:-. NITRO[2]:2. POLAR[2]:+. POSNG[2]:0. SMALL[2]:-. SULPH[2]:-. TEENY[2]:-. CRING[2]:-. VALEN[2]:2. AMINO[3]:Q. ALIPH[3]:+. AROMA[3]:-. CBETA[3]:-. CHARG[3]:-. COVAL[3]:-. HBOND[3]:+. HPHOB[3]:-. IONIZ[3]:-. NITRO[3]:2. POLAR[3]:+. POSNG[3]:0. SMALL[3]:-. SULPH[3]:-. TEENY[3]:-. CRING[3]:-. VALEN[3]:2. AMINO[4]:R. ALIPH[4]:+. AROMA[4]:-. CBETA[4]:-. CHARG[4]:+. COVAL[4]:-. HBOND[4]:+. HPHOB[4]:-. IONIZ[4]:+. NITRO[4]:4. POLAR[4]:+. POSNG[4]:+. SMALL[4]:-. SULPH[4]:-. TEENY[4]:-. CRING[4]:-. VALEN[4]:3. AMINO[5]:Q. ALIPH[5]:+. AROMA[5]:-. CBETA[5]:-. CHARG[5]:-. COVAL[5]:-. HBOND[5]:+. HPHOB[5]:-. IONIZ[5]:-. NITRO[5]:2. POLAR[5]:+. POSNG[5]:0. SMALL[5]:-. SULPH[5]:-. TEENY[5]:-. CRING[5]:-. VALEN[5]:2. AMINO[6]:Q. ALIPH[6]:+. AROMA[6]:-. CBETA[6]:-. CHARG[6]:-. COVAL[6]:-. HBOND[6]:+. HPHOB[6]:-. IONIZ[6]:-. NITRO[6]:2. POLAR[6]:+. POSNG[6]:0. SMALL[6]:-. SULPH[6]:-. TEENY[6]:-. CRING[6]:-. VALEN[6]:2. AMINO[7]:Q. ALIPH[7]:+. AROMA[7]:-. CBETA[7]:-. CHARG[7]:-. COVAL[7]:-. HBOND[7]:+. HPHOB[7]:-. IONIZ[7]:-. NITRO[7]:2. POLAR[7]:+. POSNG[7]:0. SMALL[7]:-. SULPH[7]:-. TEENY[7]:-. CRING[7]:-. VALEN[7]:2. AMINO[8]:Q. ALIPH[8]:+. AROMA[8]:-. CBETA[8]:-. CHARG[8]:-. COVAL[8]:-. HBOND[8]:+. HPHOB[8]:-. IONIZ[8]:-. NITRO[8]:2. POLAR[8]:+. POSNG[8]:0. SMALL[8]:-. SULPH[8]:-. TEENY[8]:-. CRING[8]:-. VALEN[8]:2. MULT3:7. MULT5:4. MULT7:3. MULT9:2. 2GRAM:IA. GRAM2:HQ. 3GRAM:CIA. GRAM3:HQQ. Bioinformatics Tony C Smith

  37. demo Bioinformatics Tony C Smith

  38. Concluding remarks • A [pseudo] text classification approach to sequence prediction problems can perform as well as the state-of-the-art stochastic methods • Allows miscellaneous facts (i.e. any textual description of relevant information) to be included • A ranked list of features from the text classifier provides insights into the underlying biology • Features could be used for text generation Bioinformatics Tony C Smith

  39. Biotechnology • Biologists know proteins, computer scientists know machine learning • Together, they can find out a lot of hidden information about genes and proteins • Biotechnology is a multi-billion dollar industry • Biotechnology is one of the best funded areas of scientific research Bioinformatics Tony C Smith

  40. The University of Waikato • Waikato University is the centre of the universe for machine learning • The Machine Learning Group is a large, globally active, well-funded research group • The WEKA workbench of ML tools is used around the world • Professors at Waikato University literally wrote the book on sequence modeling Bioinformatics Tony C Smith

  41. The University of Waikato If you’re seriously interested in machine learning, in getting involved in bioinformatics research, or indeed any other area along the leading edge of computer science, then university is the only place to be, and Waikato wants You! Bioinformatics Tony C Smith

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