🔍 What is this about?

Data science & machine learning (ML) are transforming biology by helping us find hidden patterns in huge datasets — from DNA sequences to patient health records.

In bioinformatics, ML helps:
✅ Predict disease risk,
✅ Classify cancer types,
✅ Find new drug targets,
✅ And even forecast how microbes might evolve.

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🧠 Basics of supervised vs. unsupervised learning

Type of learning	What it means	Examples in bioinformatics
Supervised learning	Model learns from labeled data (you know the answers)	Classify tumors as benign vs. malignant, predict patient survival.
Unsupervised learning	Model finds natural patterns in unlabeled data	Cluster gene expression profiles to find unknown subtypes of cancer.

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🔄 Mini workflow: how ML is used in bioinformatics

javaCopyEdit1. Collect data
   (gene expression, mutations, clinical data)
      ↓
2. Preprocess data
   (clean, normalize, remove noise)
      ↓
3. Choose ML method
   (supervised for prediction, unsupervised for clustering)
      ↓
4. Train model
      ↓
5. Test & validate
      ↓
6. Biological interpretation
   (find key genes, pathways, predict outcomes)

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📝 Short case study: Classifying cancer using gene expression data

🧬 Scenario

• Researchers have gene expression profiles of patients with different cancer types.

💻 What they do

• Use Python (libraries like Pandas, Scikit-learn).
• Apply supervised learning (e.g. support vector machines or random forests) to train a model to classify samples as breast vs. lung vs. colon cancer based on expression levels.

🎯 Why this matters

• Helps doctors diagnose faster or predict which patients will respond to a treatment, moving toward personalized medicine.

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✅ Short summary table

ML method	Used for	Example bioinformatics application
Supervised (classification, regression)	Predict labels / outcomes	Predict cancer subtype from RNA-seq data.
Unsupervised (clustering, PCA)	Find hidden patterns, group samples	Discover new disease subtypes from gene profiles.
Feature selection	Find most important genes/features	Identify top 10 genes distinguishing cancer vs. normal.