Course details
OriGene Biosolutions is a premier life science research and training organization dedicated to advancing bioinformatics and data analysis. Tailored solutions for your bioinformatics needs.
Professional Certification in Machine Learning & AI for Biotechnology and Bioinformatics
Level
Advanced
Advanced
Duration
3 Months | 12 hours per week
3 Months | 12 hours per week
Flexible Schedule
Learn from anywhere.
Learn from anywhere.
Modules
7
7
Capsote Project
Real-world project with publication.
Real-world project with publication.
Course Modules
Enroll Now- What is ML/AI? Differences and overlap
- Types of ML: Supervised, unsupervised, reinforcement
- Biological data characteristics and preprocessing challenges
- Data formats: FASTA, VCF, GTF, expression matrices
- Handling missing data, scaling, encoding
- Feature selection for omics datasets
- Biological feature representation: one-hot, k-mer, embedding
- Dimensionality reduction (PCA, t-SNE, UMAP)
- Classification (SVM, Random Forest, Logistic Regression)
- Regression (Linear, Ridge, Lasso)
- Clustering (k-means, hierarchical, DBSCAN)
- Model evaluation: accuracy, ROC, confusion matrix, cross-validation
- Gene expression classification and clustering
- Disease subtype prediction (e.g., cancer, rare diseases)
- Pathway analysis and gene set enrichment with ML
- Case study: Breast cancer expression profiling
- Neural networks basics and architectures
- CNNs for sequence and structure prediction
- RNNs/LSTMs for time-series or sequence modeling
- Case study: Protein structure/function prediction
- Virtual screening with ML models
- Predicting drug-target interactions
- Toxicity and ADMET prediction
- AI-driven biomarker discovery and patient stratification
- Model deployment with Streamlit or Flask
- Bioinformatics dashboard creation
- Final project: Apply ML to a biological dataset (RNA-seq, drug data, etc.)
- Discussion on ethics and explainability in AI for biology
Course Overview
This course provides an in-depth understanding of how machine learning and artificial intelligence are applied in genomics, drug discovery, systems biology, and biomedical informatics. It balances theory, practical coding, and real-world case studies to empower participants to solve biological problems using AI-driven solutions.
This course provides an in-depth understanding of how machine learning and artificial intelligence are applied in genomics, drug discovery, systems biology, and biomedical informatics. It balances theory, practical coding, and real-world case studies to empower participants to solve biological problems using AI-driven solutions.
What You'll Learn
ML fundamentals and workflow design for biological datasets
Applications of supervised and unsupervised learning in omics
AI tools for gene expression analysis and disease prediction
Deep learning for protein structure and drug discovery
Hands-on projects with Python, scikit-learn, TensorFlow, and bioML libraries
Who Can Register
UG/PG life science or CS students, PhD scholars, researchers, and biotech/bioinformatics professionals with interest in AI applications.
UG/PG life science or CS students, PhD scholars, researchers, and biotech/bioinformatics professionals with interest in AI applications.
Prerequisites
Basic biology (genes, proteins, omics)
Introductory Python programming
Interest in statistics or data science