Computational Biology та обчислювальна біологія

🎓 Areas

Genomics

Sequence Analysis: Alignment, assembly, annotation.

Variant Calling: Detection of mutations, SNPs.

Expression Analysis: RNA-seq, differential expression.

Proteomics

Structure Prediction: AlphaFold, protein folding.

Function Prediction: Annotation, classification.

Interactions: Protein-protein, protein-drug.

Drug Discovery

Property Prediction: ADMET, toxicity, activity.

Target Identification: Drug-target interactions.

De Novo Design: Generation of new molecules.

🔧 Machine Learning Methods

Sequence Models

LSTM/GRU: For sequences (DNA, proteins).

Transformers: BERT for sequences, ProtBERT for proteins.

CNN: For sequence patterns.

Graph Neural Networks

Molecules: GNN for molecular graphs.

Networks: GNN for biological networks.

Applications: Drug discovery, network analysis.

AlphaFold

Concept: Deep learning for protein structure prediction.

Revolution: Accuracy of structure from sequences.

Applications: Protein structure, drug design.

📚 Practical Examples

Example 1: Protein Structure Prediction with AlphaFold

Sequence: Prepare the protein sequence.

AlphaFold: Use AlphaFold for prediction.

Validation: Compare with experimental structure.

Example 2: Drug Property Prediction with GNN

Graph: Create a molecular graph.

GNN: Train GNN for property prediction.

Prediction: Predict properties of new molecules.