Omics to Insight: AI-Driven Molecular Diagnostics & Intelligent Primer Engineering
Transform Omics Data into Precision Biotechnology Insights with AI
Skills you will gain:
About Program:
This workshop introduces participants to the design of end-to-end AI pipelines for omics data, covering data cleaning, integration, feature engineering, predictive modeling, validation, and result interpretation. Participants will explore how AI supports applications such as biomarker discovery, strain optimization, disease classification, and precision biotechnology product development. The focus is on practical, dry-lab workflows using real-world biological datasets and reproducible computational tools.
Aim: This workshop aims to train participants in building AI-driven pipelines that transform raw omics data into actionable biological and biotechnological insights. It focuses on integrating genomics, transcriptomics, proteomics, and related datasets with machine learning workflows for prediction, classification, and decision-making.
Program Objectives:
- Understand the structure and challenges of multi-omics datasets.
- Learn to build AI workflows for preprocessing, integration, and feature extraction.
- Apply machine learning models for prediction and biological classification.
- Evaluate pipeline performance with statistical and biological validation.
- Interpret AI outputs for biomarker discovery and precision biotechnology decisions.
What you will learn?
Day 1: Foundations – Omics Data & AI in Biotechnology
- Genomics, Transcriptomics, Proteomics, Metabolomics overview
- Role of omics in modern diagnostics
- Case studies in precision medicine
- Data Acquisition & Biological Databases
- NCBI, EMBL-EBI, UniProt overview, Sequence formats (FASTA, FASTQ)
- Data retrieval and preprocessing
- Applications in molecular diagnostics
- AI workflow in biological data analysis
- Hands-on: Retrieving sequence data from public databases
- Basic Python setup for bioinformatics
- Data cleaning and formatting
Day 2: AI Pipelines & Molecular Diagnostics
- Bioinformatics Pipelines
- Sequence alignment (BLAST, Clustal), Variant analysis basics
- Pipeline architecture (input → processing → output)
- AI Models for Molecular Diagnostics
- Classification models (disease vs normal)
- Feature selection from omics data
- Hands-on: Building a simple ML model (classification)
- Using Python libraries (NumPy, Pandas, Scikit-learn) &Interpreting model outputs
Day 3: Primer Engineering & Integrated Applications
- PCR basics and primer requirements
- GC content, melting temperature (Tm)
- Avoiding dimers and secondary structures
- Intelligent Primer Engineering
- AI-assisted primer design
- Tools (Primer3, BLAST validation)
- Specificity and optimization strategies
- Workflow from data → diagnosis → validation
- Hands-on: Designing primers using software tools & In silico validation of primers
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Undergraduate/postgraduate degree in Bioinformatics, Biotechnology, Computational Biology, Genomics, Molecular Biology, Data Science, or related fields.
- Professionals working in biotech, pharma, healthcare analytics, diagnostics, or omics research sectors.
- Data scientists and AI/ML engineers interested in applying machine learning to biological and biotechnology datasets.
- Individuals with a keen interest in precision biotechnology, omics analytics, and AI-driven discovery.
Career Supporting Skills
Program Outcomes
Participants will be able to:
- Build AI pipelines for handling and analyzing omics datasets.
- Integrate multiple biological data layers into predictive workflows.
- Apply machine learning models to identify patterns and biomarkers.
- Interpret results in a biologically meaningful and translational context.
- Create reproducible omics-to-insight pipelines for research and industry use.