Hands-On AI Tools for Modern Bioinformatics Research
Unlock the Power of AI for Biological Discovery and Precision Medicine
About This Course
The integration of AI and machine learning with bioinformatics has transformed how researchers process, analyze, and interpret biological data. Genomic sequencing, proteomics data, and clinical records are growing at an exponential rate, making traditional methods inadequate for understanding complex patterns and relationships. AI tools such as deep learning, random forests, and support vector machines are now essential for making sense of vast datasets in a meaningful way.
This workshop provides hands-on training in applying AI tools to bioinformatics challenges. Participants will learn to use Python libraries such as Scikit-learn, TensorFlow, Keras, Pandas, and NumPy to preprocess data, train models, and validate predictions. The program emphasizes workflow automation, model evaluation, and the interpretability of AI models to enhance the understanding of biological systems. Participants will work with real-world datasets from genomics, transcriptomics, and clinical studies to explore disease mechanisms, identify biomarkers, and predict therapeutic responses.
Aim
This workshop aims to provide participants with practical experience in applying AI and machine learning tools to bioinformatics research. It focuses on data analysis, pattern recognition, and predictive modeling in the context of biological data, including genomics, transcriptomics, and proteomics. Participants will gain hands-on experience with Python-based AI tools, enabling them to efficiently handle biological datasets, build machine learning models, and extract biological insights for precision medicine, biomarker discovery, and disease research.
Workshop Objectives
- Understand key AI and machine learning tools for bioinformatics data analysis.
- Learn to preprocess biological datasets such as genomic, transcriptomic, and proteomic data.
- Apply supervised and unsupervised learning techniques to solve bioinformatics problems.
- Use deep learning models for sequence analysis, classification, and regression tasks.
- Evaluate model performance and interpret results using AI in biomarker discovery and disease research.
Workshop Structure
Day 1: Introduction to AI Tools in Bioinformatics
- Introduction to bioinformatics concepts, challenges, and the growing role of AI in biological research
- Tools for handling and analyzing genomic, transcriptomic, and proteomic data (FASTA, FASTQ, VCF, GFF)
- Techniques for cleaning and normalizing raw biological data, handling missing values, and preparing data for analysis
- Key databases (NCBI, Ensembl, UniProt), tools for querying and retrieving biological data
- Introduction to essential bioinformatics tools and libraries: Biopython, Scikit-learn, Keras/TensorFlow, RDKit, Pandas, NumPy
Tools: Python, Biopython, Pandas, NumPy, Jupyter/Colab, Matplotlib, Scikit-learn
Day 2: AI for Genomics, Transcriptomics, and Proteomics
- Using machine learning models for gene expression analysis, variant calling, and genome-wide association studies (GWAS)
- Deep learning applications for RNA-Seq data analysis, differential gene expression analysis, and identification of biomarkers
- Application of machine learning for protein function prediction, structure prediction (AlphaFold), and protein-protein interaction (PPI) networks
- Techniques for classification, regression, clustering, and dimensionality reduction applied to biological datasets
- Tools: Scikit-learn, Keras/TensorFlow, RDKit, PyTorch, Jupyter/Colab, Seaborn/Matplotlib
Day 3: Advanced AI Applications and Real-World Research
- Using deep learning algorithms (AlphaFold, DeepMind) for predicting protein structures and function
- Leveraging AI to build QSAR models, virtual screening, and drug-target interaction prediction
- Using Natural Language Processing (NLP) for mining biological literature, abstract classification, and identifying gene-disease associations
- Evaluating model performance using cross-validation, ROC-AUC, F1-score, and understanding model interpretability (SHAP, LIME)
- Introduction to deploying AI models for real-time data analysis, building simple web interfaces for bioinformatics applications
- Tools: TensorFlow/Keras, PyTorch, Scikit-learn, SHAP, LIME, Streamlit (optional), Jupyter/Colab
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/09/2026
IST 07:00 PM
Workshop Dates
02/09/2026 – 02/11/2026
IST 08:00 PM
Workshop Outcomes
Participants will be able to:
- Preprocess and analyze genomic, transcriptomic, and proteomic data using AI tools.
- Build and evaluate AI models for classification, regression, and predictive analytics in bioinformatics.
- Understand AI model evaluation metrics and interpret biological predictions.
- Apply AI techniques to real-world bioinformatics datasets for biomarker identification and disease research.
- Automate workflows and interpret model results to drive insights into personalized medicine.
Fee Structure
Student Fee
₹1799 | $65
Ph.D. Scholar / Researcher Fee
₹2799 | $75
Academician / Faculty Fee
₹3799 | $85
Industry Professional Fee
₹4799 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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