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Napovedovanje imunskega odziva iz peptidnih mikromrež

Explore a study on predicting immune responses using peptide microarrays and machine learning techniques. Understand how epitopes, antibodies, and peptide arrays play a crucial role in immune response prediction.

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Napovedovanje imunskega odziva iz peptidnih mikromrež

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  1. Napovedovanje imunskega odzivaiz peptidnih mikromrež Mitja Luštrek1 (2), Peter Lorenz2, Felix Steinbeck2, Georg Füllen2, Hans-Jürgen Thiesen2 1 Odsek za inteligentne sisteme, Institut Jožef Stefan 2 Univerza v Rostocku

  2. Introduction • Immune response prediction • Interpretation

  3. Introduction • Immune response prediction • Interpretation

  4. Peptide = part of protein = short sequence of amino acids Image taken from EMBL website

  5. Peptide = part of protein = short sequence of amino acids SNDIVLT Image taken from EMBL website = string of letters from 20-letter alphabet (1 letter = 1 amino acid, 20 standard amino acids)

  6. Epitope Antigen protein Antibody binding Antibody

  7. Epitope Epitope Antigen protein Antibody binding Antibody

  8. Epitope Epitope Antigen protein Peptide

  9. Epitope Epitope Antigen protein

  10. Epitope Epitope Antigen protein Antibody binding Antibody

  11. Epitope Epitope Antigen protein Antibody binding Antibody

  12. Epitope Epitope Antigen protein Antibody binding Antibody

  13. Epitope Epitope Antigen protein

  14. Epitope Epitope Antigen protein

  15. Peptide arrays Peptide array Peptides (15 amino acids) Glass slide

  16. Peptide arrays IVIg antibody mixture Peptide array Peptides (15 amino acids) Glass slide

  17. Peptide arrays IVIg antibody mixture Red = epitopes (bind antibodies) Black = non-epitopes Peptide array Peptides (15 amino acids) Glass slide

  18. Peptide arrays Red = epitopes (bind antibodies) Black = non-epitopes Antibody against antibody + dye Antibody Peptide Glass slide

  19. Peptide arrays Red = epitopes (bind antibodies) Black = non-epitopes

  20. Introduction • Immune response prediction • Interpretation

  21. Our task

  22. Our task Machine learning

  23. Our task Machine learning Training set: 13,638 peptides (3,420 epitopes) Test set: 13,640 peptides (3,421 epitopes) Balanced until the final testing

  24. Machine learning

  25. Machine learning Attribute representation

  26. Machine learning Attribute representation ML Classifier Proability for epitope p

  27. Machine learning Attribute representation ML Classifier Proability for epitope p

  28. Machine learning Attribute representation 1 Attribute representation 8 ... ML ML Classifier 1 ... Classifier 8

  29. Machine learning Attribute representation 1 Attribute representation 8 ... ML ML Final proability for epitope p Classifier 1 ... Classifier 8 Meta classifier ML

  30. Machine learning SVM (SMO), Logistic regression Attribute representation 1 Attribute representation 8 ... ML ML Final proability for epitope p Classifier 1 ... Classifier 8 Linear regression Meta classifier ML

  31. Attribute representation 1 Amino-acid counts

  32. Attribute representation 2 Amino-acid count differences

  33. Attribute representation 3 Subsequence counts

  34. Attribute representation 4 Amino-acid class counts

  35. Attribute representation 5 Amino-acid class subsequence counts

  36. Attribute representation 6 Amino-acid pair counts Rationale: antibodies may bind in two places due to their two-chain structure. Antibody Peptide

  37. Attribute representation 6 Amino-acid pair counts Rationale: antibodies may bind in two places due to their two-chain structure. Antibody 3 3 1 2 Peptide

  38. Attribute representation 7 Amino-acids at distances from first + first amino acid Rationale: antibodies may bind in two places, first amino acid most accesible on the peptide array. Antibody Peptide

  39. Attribute representation 7 Amino-acids at distances from first + first amino acid Rationale: antibodies may bind in two places, first amino acid most accesible on the peptide array. Antibody Peptide

  40. Attribute representation 8 Average amino-acid properties

  41. Attribute representation 9 (not used) Amino-acid countswith a difference Equivalent for epitope prediction?

  42. Attribute representation 9 (not used) Amino-acid countswith a difference Equivalent for epitope prediction? • Count F as: • 1 F • 0.8 W • 0.4 Y • ... • Count W as: • 1 W • 0.7 F • 0.3 Y • ...

  43. Attribute representation 9 (not used) Amino-acid substitution matrix

  44. Attribute representation 9 (not used) Amino-acid substitution matrix Optimize with a genetic algorithm to maximize classification accuracy

  45. Results – training set

  46. Results – training set

  47. Results – test set

  48. Results – test set Epitope : non-epitope = 1 : 1 Epitope : non-epitope = 1 : 3

  49. Results – test set State of the art: SVM + string kernel (EL-Manzalawy et al., 2008) Trained and tested on our data.

  50. Results – test set Our results Balanced: 0.883 / 83.7 % Original: 0.884 / 85.9 % EL-Manzalawy Balanced: 0.868 / 82.0 % Original: 0.874 / 83.9 %

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