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ISLab Intelligent Systems Lab Piet van Remortel - PowerPoint PPT Presentation

May 21, 2023 •296 likes •1.07k views

ISLab Intelligent Systems Lab Piet van Remortel piet.vanremortel@ua.ac.be 1 Overview Who we are What we do Applied Machine learning Bio-informatics What we look for 2 Who ? Prof. Dr. Alain Verschoren Who we are Dr. Piet van Remortel

  1. ISLab Intelligent Systems Lab Piet van Remortel piet.vanremortel@ua.ac.be 1

  2. Overview Who we are What we do Applied Machine learning Bio-informatics What we look for 2

  3. Who ? Prof. Dr. Alain Verschoren Who we are Dr. Piet van Remortel What we do Applied Koenraad Van Leemput ML Tim Van den Bulcke (with ESAT-KUL) Bio-i Dr. Elmer Fernandez (Arg.) What we look for 3

  4. What we do Who we are (Applied) machine learning What we do History in evolutionary computation Applied ML More recent: ML applications such as bio-informatics and predictive Bio-i toxicology What we look for 4

  5. Machine Learning Wikipedia says Who we are “a broad subfield of artificial intelligence” What we do “concerned with the development of algorithms and techniques” Machine Learning “which allow computers to learn” Bio-i applications search engines, bioinformatics, What we detecting credit card fraud, stock market look for analysis, classifying DNA sequences, speech and handwriting recognition, ... 5

  6. Machine Learning What is it good for? Who we are extract knowledge from given data What we do typically “hard” problems computationally hard Machine Learning enumeration of possibilities takes forever etc lack of traditional formalisms Bio-i traditional statistics What we complete mathematical models of the problem look for high dimensions, discontinuities, ... 6

  7. Genetic Algorithms Who we are Use biological evolution as a metaphor for search/ optimization What we do ‘evolve’ solutions to problems Machine is an existing biological organism a ‘solution’ to Learning the problem of surviving in the environment ? iterate Bio-i generate diversity What we select for some fitness measure look for 7

  8. Genetic Algorithms Who we are What we do Machine Learning Bio-i What we look for 8

  9. Genetic Algorithms PRO very robust : solve a problem given some Who we are fitness measure What we do actually means: can cope with non-convex fitness landscapes Machine actually means: can solve problems where Learning good solutions are not necessarily alike each other Bio-i actully means: solving the problem is What we harder then ‘climbing one hill’ in the look for landscape can be easily parallellized (CalcUA !) 9

  10. Genetic Algorithms Who we are What we do CON inherently slow Machine probabilistic in nature Learning true mechanics not well understood Bio-i ‘it just works’ (more or less) What we look for 10

  11. Genetic Algorithms What we used to do with GAs Who we are study fitness landscapes What we do predict problem hardness etc What we do now Machine Learning apply ! e.g. Feature selection in predictive Bio-i toxicology use a number of genes to characterise an What we look for unknown toxic compound 11

  12. Genetic Algorithms Typical applications of GAs: Who we are high dimensional problem What we do lots of interaction between the different elements of the solution Machine the impact of one parameter depends on the Learning value of other parameters Bio-i e.g. hot-water tap in the shower possibility to assign numerical quality to a What we candidate solution look for 12

  13. Classification A Who we are What we do B Machine Learning Bio-i C What we look for 13 13

  14. Classification A Who we are What we do ? B Machine Learning Bio-i C What we look for 13 13

  15. Classification A Who we are What we do ? B Machine Learning correct Bio-i C features What we look for 13 13

  16. Classification A Who we are What we do ? B Machine Learning Bio-i C What we look for 13 13

  17. Classification A Who we are What we do ? B Machine Learning domain Bio-i C knowledge What we look for 13 13

  18. Classification A Who we are What we do ? B Machine Learning Bio-i C What we look for 13 13

  19. Classification A Who we are What we do ? B Machine Learning characterisation Bio-i C (multiple classes) What we look for 13 13

  20. Classification of toxic compounds Cooperation with EB&T (De Coen/Blust) Who we are Goal: Characterise unknown toxic compound by means of genetic What we do expression fingerprint(s) Machine Learning 40-50 known chemicals as training set Bio-i goal: pre-compliance screening for toxic mode of action What we look for algorithmic foundation: SVM/PLS/GP 14 14

  21. Classifiers Many classifiers around Who we are prototype of two promosing techniques What we do SVM : support vector machine Machine Learning PLS : partial least squares Bio-i more to be added Genetic programming What we look for ... 15 15

  22. PLS: Partial least squares regression technique relate input and output matrix Who we are origin: chemistry (spectral analysis) What we do pro Machine handles collinear components in X Learning fast Bio-i What we Y X look for 16 16

  23. SVM: Support Vector Machines Who we are What we do Machine Learning Bio-i What we look for 17 17

  24. SVM: Support Vector Machines Who we are Classification technique What we do Machine Learning Bio-i What we look for 17 17

  25. SVM: Support Vector Machines Who we are Classification technique What we do Mathematical foundation Machine Learning Bio-i What we look for 17 17

  26. SVM: Support Vector Machines Who we are Classification technique What we do Mathematical foundation Machine origin: Statistical Learning Theory Learning Bio-i What we look for 17 17

  27. SVM: Support Vector Machines Who we are Classification technique What we do Mathematical foundation Machine origin: Statistical Learning Theory Learning Bio-i bottom line: linear separability by projection to high-dimensional space What we look for 17 17

  28. Linear separability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 18 18

  29. Linear separability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 18 18

  30. Linear separability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 18 18

  31. Linear separability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 18 18

  32. Linear separability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 18 18

  33. Linear separability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 18 18

  34. Linear inseparability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 19 19

  35. Linear inseparability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 19 19

  36. Linear inseparability (in 2D) Who we are What we do ??? Machine Learning Bio-i What we look for 19 19

  37. Linear inseparability (in 2D) Who we are What we do Machine Learning Bio-i What we look for 20 20

  38. Linear inseparability (in 3D) Who we are What we do Machine Learning Bio-i What we look for 21 21

  39. Linear inseparability (in 3D) Who we are What we do Machine Learning Bio-i What we look for 21 21

  40. Remember ... Goal: Characterise unknown toxic compound by means of genetic expression fingerprint(s) 22 22

  41. Remember ... Goal: Characterise unknown toxic compound by means of genetic expression fingerprint(s) 22 22

  42. Gene expression Who we are What we do Machine Learning Bio-i What we look for 23

  43. Gene expression Who we are What we do Machine Learning Bio-i What we look for 23

  44. Gene expression Who we are What we do Machine Learning Bio-i What we look for 23

  45. Gene expression Who we are What we do Machine Learning Bio-i What we look for 23

  46. Gene expression Who we are What we do Machine Learning Bio-i What we look for 23

  47. Microarray technology Who we are What we do Machine Learning Bio-i What we look for 24

  48. Infering transcriptional regulatory networks Who we are What we do Machine Learning Bio-i What we look for microarray causal relations data between genes 25

  49. A transcriptional regulatory network Who we are What we do Machine Learning Bio-i What we look for 26

  50. A transcriptional regulatory network Who we are What we do Machine Learning Bio-i What we look for 27

  51. Systems Biology Systems biology is an academic Who we are field that seeks to integrate different levels of information to What we do understand how biological systems Machine function. Learning Bio-i What we look for 28

  52. Systems Biology Systems biology is an academic Who we are field that seeks to integrate different levels of information to What we do understand how biological systems Machine function. Learning Bio-i What we look for 28

  53. Systems Biology Systems biology is an academic Who we are field that seeks to integrate different levels of information to What we do understand how biological systems Machine function. Learning Bio-i transcriptome What we look for 28

  54. Systems Biology Systems biology is an academic Who we are field that seeks to integrate different levels of information to What we do understand how biological systems Machine function. Learning Bio-i transcriptome proteome What we look for 28

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