Bioinformatics History and Introduction - Chagall

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Comparison of TargetMiner with othertarget prediction algorithms24 January 2013 Luce SkrabanekBandyopadhyay and Mitra Bioinformatics 2009
Further evaluation• Trained other SVMs with their negative dataset –found that they improved the accuracy• Trained their 90-feature SVM with a negativedataset made in the traditional way anddecreased the accuracy24 January 2013 Luce Skrabanek
Page 1 and 2: BioinformaticsHistory and Introduct
Page 3 and 4: Required skills• Biology• Compu
Page 5 and 6: Applications of bioinformatics• M
Page 7 and 8: The scales of knowledge• Historic
Page 9 and 10: Biology of Systems• Huge amounts
Page 11 and 12: 24 January 2013 Luce SkrabanekRavi
Page 13 and 14: Where do we get our data from?• E
Page 15 and 16: Caution!• Errors in the databases
Page 17 and 18: My protein doesn’t begin with an
Page 19 and 20: Some sample error rates• GenBank:
Page 21 and 22: Propagation of errors• The qualit
Page 23 and 24: miRNAs: an introduction• Small RN
Page 25 and 26: miRNA3’ UTR24 January 2013 Luce S
Page 27 and 28: Indirect experimental identificatio
Page 29 and 30: Computational prediction of miRNA t
Page 31 and 32: Pipeline to identify miRNA targets
Page 33 and 34: Sequence matching algorithm• Modi
Page 35 and 36: Control sequences• 100 sets of ra
Page 37 and 38: Pipeline to identify mammalian targ
Page 39 and 40: Validation• Luciferase reporter a
Page 41 and 42: 24 January 2013 Luce SkrabanekLewis
Page 43 and 44: Many other programs…• Many prog
Page 45 and 46: Evaluation comparison5 miRNAs vs. S
Page 47 and 48: SVMs: Brief introduction• Positiv
Page 49: TargetMiner• Used the 289 negativ
Page 53: Important points• This type of an

Bioinformatics History and Introduction - Chagall

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