1. Lecture 1: Introduction to Data Mining for Bioinformatics Fall 2005 Peter van der Putten (putten_at_liacs.nl) Databases and Data Mining

2. Course Outline Objective –Understand the basics of data mining –Gain understanding of the potential for applying it in the bioinformatics domain –Limited hands on experience Schedule Evaluation –Practical assignment (2 nd ) plus take home exercise

3. Agenda Today What is data mining? A short summary of life Data mining revisited

4. What is data mining?

5. Genomic Microarrays – Case Study Problem: –Leukemia (different types of Leukemia cells look very similar) –Given data for a number of samples (patients), can we Accurately diagnose the disease? Predict outcome for given treatment? Recommend best treatment? Solution –Data mining on micro-array data

6. Example: ALL/AML data 38 training patients, 34 test patients, ~ 7,000 patient attributes (micro array gene data) 2 Classes: Acute Lymphoblastic Leukemia (ALL) vs Acute Myeloid Leukemia (AML) Use train data to build diagnostic model ALLAML Results on test data: 33/34 correct, 1 error may be mislabeled

7. Sources of (artificial) intelligence Reasoning versus learning Learning from data –Patient data –Customer records –Stock prices –Piano music –Criminal mug shots –Websites –Robot perceptions –Etc.

8. Some working definitions…. ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably Data mining = –The process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data Multidisciplinary field originating from artificial intelligence, pattern recognition, statistics, machine learning, bioinformatics, econometrics, ….

9. A short summary of life Bio Building Blocks Biotech Data Mining Applications

10. The Promise….....

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13. DNA, Proteins, Cells

16. From DNA to Proteins

17. Discovering the structure of DNA James Watson & Francis Crick - Rosalind Franklin

18. The structure of DNA

19. DNA Trivia DNA stores instructions for the cell to peform its functions Double helix, two interwoven strands Each strand is a sequence of so called nucleotides Deoxyribonucleic acid (DNA) comprises 4 different types of nucleotides (bases): adenine (A), thiamine (T), cytosine (C) and guanine (G) –Nucleotide uracil (U) doesn’t occur in DNA Each strand is reverse complement of the other Complementary bases –A with T –C with G

20. DNA Trivia Each nucleus contain 3 x 10^9 nucleotides Human body contains 3 x 10^12 cells Human DNA contains 26k expressed genes, each gene codes for a protein in principle DNA of different persons varies 0.2% or less Human DNA contains 3.2 x 10^9 base pairs –  X-174 virus: 5,386 –Salamander: 100  10 9 –Amoeba dubia: 670  10 9

21. Primary Protein Structure Proteins are built out of peptides, which are poylmer chains of amino acids Twenty amino acids are encoded by the standard genetic code shared by nearly all organisms and are called standard amino acids (100 amino acids exist in nature)

22. Protein Structure from Primary to Quaternary

23. Proteins: 3D Structure A representation of the 3D structure of myoglobin, showing coloured alpha helices. This protein was the first to have its structure solved by X-ray crystallography by Max Perutz and Sir John Cowdery Kendrew in 1958, which led to them receiving a Nobel Prize in Chemistry. http://en.wikipedia.org/wiki/Protein

24. Proteins: 3D Structure Molecular surface of several proteins showing their comparative sizes. From left to right are: Antibody (IgG), Hemoglobin, Insulin (a hormone), Adenylate Kinase (an enzyme), and Glutamine Synthetase (an enzyme).

25. Proteins: 3D Structure G Protein-Coupled Receptors (GPCR) represent more than half the current drug targets

26. DNA Codes for Proteins but Proteins also Control Gene Expression Protein regulation occurs at each step of synthesis

27. Repressor Protein Switching Genes On and Off

28. Regulatory Protein Coordinating Gene Expression

29. Importance of Combinatorial Gene Control combinations of a few gene regulatory proteins can generate many different cell types during development

30. Some working definitions…. Bioinformatics = –Bioinformatics is the research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data [http://www.bisti.nih.gov/]. –Or more pragmatic: Bioinformatics or computational biology is the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems [Wikipedia Nov 2005]

31. NCBI Tools for data mining: –Nucleotide sequence analysis –Proteine sequence analysis –Structures –Genome analysis –Gene expression Data mining or not?.

32. Bio informatics and data mining From sequence to structure to function Genomics (DNA), Transcriptomics (RNA), Proteomics (proteins), Metabolomics (metabolites) Pattern matching and search Sequence matching and alignment Structure prediction –Predicting structure from sequence –Protein secondary structure prediction Function prediction –Predicting function from structure –Protein localization Expression analysis –Genes: micro array data analysis etc. –Proteins Regulation analysis

33. Bio informatics and data mining Classical medical and clinical studies Medical decision support tools Text mining on medical research literature (MEDLINE) Spectrometry, Imaging Systems biology and modeling biological systems Population biology & simulation Spin Off: Biological inspired computational learning –Evolutionary algorithms, neural networks, artificial immune systems

34. Examples of my related research Topology preserving property of self-organizing maps –Neural network for clustering & classification inspired by cortical maps Benchmarking Artificial Immune Systems Predicting throat cancer survival rate –Value of fusing data from various sources for this purpose Automated recognition of sick yeast cells in images (with prof. Verbeek) Recommender systems in bioinformatics –Amazon.com style recommendations

35. Data mining revisited

36. Some working definitions…. ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably Data mining = –The process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data Multidisciplinary field originating from artificial intelligence, pattern recognition, statistics, machine learning, bioinformatics, econometrics, ….

37. Some working definitions…. Concepts: kinds of things that can be learned –Aim: intelligible and operational concept description –Example: the relation between patient characteristics and the probability to be diabetic Instances: the individual, independent examples of a concept –Example: a patient, candidate drug etc. Attributes: measuring aspects of an instance –Example: age, weight, lab tests, microarray data etc Pattern or attribute space

38. Data mining tasks Predictive data mining –Classification: classify an instance into a category –Regression: estimate some continuous value Descriptive data mining –Matching & search: finding instances similar to x –Clustering: discovering groups of similar instances –Association rule extraction: if a & b then c –Summarization: summarizing group descriptions –Link detection: finding relationships –…

39. Data Mining Tasks: Search f.e. agef.e. weight Finding best matching instances Every instance is a point in pattern space. Attributes are the dimension of an instance, f.e. Age, weight, gender etc. Pattern spaces may be high dimensional (10 to thousands of dimensions)

40. Data Mining Tasks: Clustering f.e. agef.e. weight Clustering is the discovery of groups in a set of instances Groups are different, instances in a group are similar In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user

41. Data Mining Tasks: Clustering f.e. agef.e. weight Clustering is the discovery of groups in a set of instances Groups are different, instances in a group are similar In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user In >3 dimensions this is not possible

42. Data Mining Tasks: Classification ageweight Goal classifier is to seperate classes on the basis of known attributes The classifier can be applied to an instance with unknow class For instance, classes are healthy (circle) and sick (square); attributes are age and weight

43. Examples of Classification Techniques Majority class vote Machine learning & AI Decision trees Nearest neighbor Neural networks Genetic algorithms / evolutionary computing Artificial Immune Systems Good old statistics …..

44. Example Classification Algorithm 1 Decision Trees 20000 patients age > 67 18800 patients gender = male? 1200 patients Weight > 85kg 800 customers Diabetic (%10) etc. 400 patients Diabetic (%50) no yes no

45. Decision Trees in Pattern Space ageweight Goal classifier is to seperate classes (circle, square) on the basis of attribute age and income Each line corresponds to a split in the tree Decision areas are ‘tiles’ in pattern space

46. Example classification algorithm 3: Neural Networks Inspired by neuronal computation in the brain (McCullough & Pitts 1943 (!)) Input (attributes) is coded as activation on the input layer neurons, activation feeds forward through network of weighted links between neurons and causes activations on the output neurons (for instance diabetic yes/no) Algorithm learns to find optimal weight using the training instances and a general learning rule.

47. Example simple network (2 layers) Probability of being diabetic = f (age * weight age + body mass index * weight body mass index) Neural Networks Weight body mass index Probability of being diabetic age body_mass_index weight age

48. Neural Networks in Pattern Space Classification f.e. agef.e. weight Simpel network: only a line available (why?) to seperate classes Multilayer network: Any classification boundary possible

49. Descriptive data mining: association rules Discovery of interesting patterns Rule format: if A (and B and C etc) then Z Example: –If customer buys potatoes (A) and sauerkraut (B) then customer buys sausage (Z) Important measures –Support condition: how often do potatoes and sauerkraut occur together (A,B) –Confidence rule: how often do sausages then occur / support conditions (is A,B  C always true?) Could be used for instance for mining gene expression data

50. Quiz Question

51. What have we learned today An introduction into applying data mining for bioinformatics A short history of life Basic data mining concepts