AI-Driven Bioinformatics: From Genomic Data to Intelligent Biological Discovery

• Understand the fundamentals of biological data and bioinformatics.
• Learn how genomic, transcriptomic, and proteomic data are generated and analyzed.
• Explore major biological databases such as NCBI, Ensembl, and UniProt.
• Perform basic sequence analysis and interpret biological data.
• Understand gene expression concepts and functional genomics.
• Learn pathway and network-based biological interpretation.
• Utilize AI tools for biological data analysis and scientific interpretation.
• Develop a complete data-to-discovery workflow using real biological datasets.

📅 Day 1: Integrated Genomic Data Mining and Variant Interpretation

• Advanced navigation of genomic, transcriptomic, proteomic, and disease databases
• Gene, transcript, isoform, exon, regulatory-region, and protein annotation
• Genome-build awareness, coordinates, identifiers, and cross-database mapping
• Sequence retrieval, similarity searching, and comparative sequence interpretation
• Variant annotation, consequence prediction, and functional-impact assessment
• AI-assisted literature mining with evidence verification and source validation

🧪 Hands-on Session

• Retrieve and integrate gene, transcript, protein, variant, and disease information for a selected biological target.
• Perform sequence-similarity and variant-impact analysis and prepare an evidence-based genomic annotation table.

🧰 Tools Covered

📅 Day 2: Transcriptomic Analysis and AI-Assisted Biomarker Discovery

• Experimental design, metadata assessment, and transcriptomic dataset selection
• Gene-expression normalization, quality assessment, and sample comparison
• Differential-expression analysis and statistical significance interpretation
• Fold change, adjusted p-value, multiple testing, and expression-pattern assessment
• Identification of candidate biomarkers and disease-associated gene signatures
• AI-assisted interpretation of expression results, biological relevance, and research hypotheses

🧪 Hands-on Session

• Analyze a disease-versus-control gene-expression dataset and identify significantly differentially expressed genes.
• Prioritize candidate biomarkers using statistical evidence, biological databases, and AI-assisted interpretation.

🧰 Tools Covered

📅 Day 3: Functional Enrichment, Network Biology and Intelligent Discovery

• Gene Ontology, pathway databases, and functional-annotation frameworks
• Over-representation analysis and enrichment-result interpretation
• Biological pathway mapping and disease-mechanism reconstruction
• Protein–protein interaction network construction and confidence assessment
• Hub-gene, functional-module, and candidate-target prioritization
• AI-assisted integration of genomic, expression, pathway, and network findings

🧪 Hands-on Session

• Perform functional enrichment and pathway analysis on a prioritized disease-associated gene list.
• Construct and interpret a protein-interaction network and prepare an AI-supported biological discovery summary.

🧰 Tools Covered

• Understand the complete workflow of modern bioinformatics from biological data acquisition to interpretation.
• Confidently navigate and extract information from major biological databases such as NCBI, Ensembl, and UniProt.
• Analyze DNA, RNA, and protein sequence data using standard bioinformatics tools.
• Interpret gene expression patterns and understand their relevance in disease and biological systems.
• Perform basic functional annotation and pathway-based analysis of gene sets.
• Understand how genomic data contributes to disease research and biomedical discoveries.
• Apply AI tools to assist in biological data interpretation, literature understanding, and research problem-solving.
• Develop a systems-level understanding of how bioinformatics supports modern biotechnology, healthcare, and drug discovery.
• Gain exposure to real-world research workflows used in academic and industry settings.

• Undergraduate and postgraduate students in Biotechnology, Bioinformatics, Genetics, Molecular Biology, Life Sciences, Pharmacy, and related fields.
• PhD scholars, researchers, academicians, and biotechnology professionals.
• Bioinformatics enthusiasts interested in genomics, AI, and computational biology.

Prerequisite: Basic knowledge of molecular biology and genetics is recommended. No prior experience in bioinformatics or programming is required.

• Live & recorded sessions
• e-Certificate upon completion
• Post-workshop query support
• Hands-on learning experience