bioinformateachers

Code archive linked to the bioinformatics-oriented educational website The Bioinformateachers

• Naive Bayes classifier – a naive introduction (with R)
• Amplicon sequencing: a quick look at the construct
• R tip: remove the last character from an array of strings
• Super-quick recap of linear mixed models

Deep Learning for Life Sciences [book]

(with applications in Python)

In the folder dlb we store a collection of Python notebooks linked to the book: these notebooks are meant to provide code and exercise to illustrate some of the concepts covered in the book:

1. K-means clustering
2. Confusion matrix [N.]
3. ROC, AUC, MCC [F.]
4. Additional model performance metrics [N.]
5. Backpropagation
6. Function approximation
7. Logistic regression with neural networks
8. Softmax regression with neural networks
9. Linear regression with neural networks [F.]
10. From basic regression to deeper NN models [F.]
11. Basic RNN [F.]
12. Specialized RNN [F.]
13. Data augmentation [N.]
14. Embeddings [F.]
15. Transfer learning [N.]
16. Feature selection [N.]
17. Opening the black box [N.]
18. Transformers? [F.]