Data Science for Biotechnology students

This book is written with KTH’s course CB2030, Systemsbiology in mind.

Supervised Machine Learning

• 1. Introduction to Machine Learning
• 2. Regression
• 3. Regularization
• 4. Multiple Regression Example
• 5. Multiple Regression Analysis of KPNA2 Gene Expression
• 6. Classification
• 7. Validation
• 8. Support Vector Machines
• 9. Example of classification
• 10. Multi-Layer Perceptrons (MLPs)

Statistics

• 11. Hypothesis Testing
• 12. Differential expression anlaysis of the TCGA breast cancer set
• 13. Multiple Hypothesis Corrections
• 14. \(q\) Value calculations in breast cancer set

Unsupervised Machine Learning

• 15. Unsupervised Machine Learning
• 16. Cluster analysis of TCGA breast cancer sets
• 17. Principal Component Analysis (PCA)
• 18. PCA analysis of two lung cancer sets
• 19. Autoencoders
• 20. VAE analysis of two lung cancer sets

Network Analysis

• 21. Pathway Analysis
• 22. Example of ORA and GSEA

Appendix

• 23. Appendix: Some Maths

This work by Lukas Käll is licensed under CC BY 4.0