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Limsoon Wong Laboratories for Information Technology Singapore

From Datamining to Bioinformatics. Limsoon Wong Laboratories for Information Technology Singapore. What is Bioinformatics?. Themes of Bioinformatics. Bioinformatics = Data Mgmt + Knowledge Discovery Data Mgmt = Integration + Transformation + Cleansing Knowledge Discovery =

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Limsoon Wong Laboratories for Information Technology Singapore

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  1. From Datamining to Bioinformatics Limsoon Wong Laboratories for Information Technology Singapore

  2. What is Bioinformatics?

  3. Themes of Bioinformatics Bioinformatics = Data Mgmt + Knowledge Discovery Data Mgmt = Integration + Transformation + Cleansing Knowledge Discovery = Statistics + Algorithms + Databases

  4. Benefits of Bioinformatics • To the patient: • Better drug, better treatment • To the pharma: • Save time, save cost, make more $ • To the scientist: • Better science

  5. From Informatics to Bioinformatics MHC-Peptide Binding (PREDICT) Protein Interactions Extraction (PIES) 8 years of bioinformatics R&D in Singapore Gene Expression & Medical Record Datamining (PCL) Cleansing & Warehousing (FIMM) Gene Feature Recognition (Dragon) Integration Technology (Kleisli) Venom Informatics 1994 1996 1998 2002 2000 ISS LIT KRDL

  6. Quick Samplings

  7. Epitope Prediction TRAP-559AA MNHLGNVKYLVIVFLIFFDLFLVNGRDVQNNIVDEIKYSE EVCNDQVDLYLLMDCSGSIRRHNWVNHAVPLAMKLIQQLN LNDNAIHLYVNVFSNNAKEIIRLHSDASKNKEKALIIIRS LLSTNLPYGRTNLTDALLQVRKHLNDRINRENANQLVVIL TDGIPDSIQDSLKESRKLSDRGVKIAVFGIGQGINVAFNR FLVGCHPSDGKCNLYADSAWENVKNVIGPFMKAVCVEVEK TASCGVWDEWSPCSVTCGKGTRSRKREILHEGCTSEIQEQ CEEERCPPKWEPLDVPDEPEDDQPRPRGDNSSVQKPEENI IDNNPQEPSPNPEEGKDENPNGFDLDENPENPPNPDIPEQ KPNIPEDSEKEVPSDVPKNPEDDREENFDIPKKPENKHDN QNNLPNDKSDRNIPYSPLPPKVLDNERKQSDPQSQDNNGN RHVPNSEDRETRPHGRNNENRSYNRKYNDTPKHPEREEHE KPDNNKKKGESDNKYKIAGGIAGGLALLACAGLAYKFVVP GAATPYAGEPAPFDETLGEEDKDLDEPEQFRLPEENEWN

  8. 1 66 100 Epitope Prediction Results • Prediction by our ANN model for HLA-A11 • 29 predictions • 22 epitopes • 76% specificity • Prediction by BIMAS matrix for HLA-A*1101 Number of experimental binders 19 (52.8%) 5 (13.9%) 12 (33.3%) Rank by BIMAS

  9. Transcription Start Prediction

  10. Transcription Start Prediction Results

  11. Medical Record Analysis • Looking for patterns that are • valid • novel • useful • understandable

  12. Gene Expression Analysis • Classifying gene expression profiles • find stable differentially expressed genes • find significant gene groups • derive coordinated gene expression

  13. Medical Record & Gene Expression Analysis Results • PCL, a novel “emerging pattern’’ method • Beats C4.5, CBA, LB, NB, TAN in 21 out of 32 UCI benchmarks • Works well for gene expressions Cancer Cell, March 2002, 1(2)

  14. Vladimir Bajic Vladimir Brusic Jinyan Li See-Kiong Ng Limsoon Wong Louxin Zhang Allen Chong Judice Koh SPT Krishnan Huiqing Liu Seng Hong Seah Soon Heng Tan Guanglan Zhang Zhuo Zhang Behind the Scene and many more: students, folks from geneticXchange, MolecularConnections, and other collaborators….

  15. Questions?

  16. A More Detailed Account

  17. Jonathan’s blocks Jessica’s blocks Whose block is this? What is Datamining? Jonathan’s rules : Blue or Circle Jessica’s rules : All the rest

  18. What is Datamining? Question: Can you explain how?

  19. The Steps of Data Mining • Training data gathering • Signal generation • k-grams, colour, texture, domain know-how, ... • Signal selection • Entropy, 2, CFS, t-test, domain know-how... • Signal integration • SVM, ANN, PCL, CART, C4.5, kNN, ...

  20. Translation Initiation Recognition

  21. A Sample cDNA 299 HSU27655.1 CAT U27655 Homo sapiens CGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG 80 CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA 160 GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA 240 CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT ............................................................ 80 ................................iEEEEEEEEEEEEEEEEEEEEEEEEEEE 160 EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE 240 EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE What makes the second ATG the translation initiation site?

  22. Signal Generation • K-grams (ie., k consecutive letters) • K = 1, 2, 3, 4, 5, … • Window size vs. fixed position • Up-stream, downstream vs. any where in window • In-frame vs. any frame

  23. Too Many Signals • For each value of k, there are 4k * 3 * 2 k-grams • If we use k = 1, 2, 3, 4, 5, we have 4 + 24 + 96 + 384 + 1536 + 6144 = 8188 features! • This is too many for most machine learning algorithms

  24. Signal Selection (Basic Idea) • Choose a signal w/ low intra-class distance • Choose a signal w/ high inter-class distance • Which of the following 3 signals is good?

  25. Signal Selection (eg., t-statistics)

  26. Signal Selection (eg., MIT-correlation)

  27. Signal Selection (eg., 2)

  28. Signal Selection (eg., CFS) • Instead of scoring individual signals, how about scoring a group of signals as a whole? • CFS • A good group contains signals that are highly correlated with the class, and yet uncorrelated with each other • Homework: find a formula that captures the key idea of CFS above

  29. Sample k-grams Selected Leaky scanning • Position –3 • in-frame upstream ATG • in-frame downstream • TAA, TAG, TGA, • CTG, GAC, GAG, and GCC Kozak consensus Stop codon Codon bias

  30. Signal Integration • kNN Given a test sample, find the k training samples that are most similar to it. Let the majority class win. • SVM Given a group of training samples from two classes, determine a separating plane that maximises the margin of error. • Naïve Bayes, ANN, C4.5, ...

  31. Results (on Pedersen & Nielsen’s mRNA)

  32. Acknowledgements • Roland Yap • Zeng Fanfan • A.G. Pedersen • H. Nielsen

  33. Questions?

  34. Common Mistakes

  35. Self-fulfilling Oracle • Consider this scenario • Given classes C1 and C2 w/ explicit signals • Use 2 to C1 and C2 to select signals s1, s2, s3 • Run 3-fold x-validation on C1 and C2 using s1, s2, s3 and get accuracy of 90% • Is the accuracy really 90%? • What can be wrong with this?

  36. Phil Long’s Experiment • Let there be classes C1 and C2 w/ 100000 features having randomly generated values • Use 2 to select 20 features • Run k-fold x-validation on C1 and C2 w/ these 20 features • Expect: 50% accuracy • Get: 90% accuracy! • Lesson: choose features at each fold

  37. Apples vs Oranges • Consider this scenario: • Fanfan reported 89% accuracy on his TIS prediction method • Hatzigeorgiou reported 94% accuracy on her TIS prediction method • So Hatzigeorgiou’s method is better • What is wrong with this conclusion?

  38. Apples vs Oranges • Differences in datasets used: • Fanfan’s expt used Pedersen’s dataset • Hatzigeorgiou’s used her own dataset • Differences in counting: • Fanfan’s expt was on a per ATG basis • Hatzigeorgiou’s expt used the scanning rule and thus was on a per cDNA basis • When Fanfan ran the same dataset and count the same way as Hatzigeorgiou, got 94% also!

  39. Questions?

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