Workshop Registration End Date :20 Apr 2026

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Virtual Workshop

Omics to Insight: Building AI Pipelines for Precision Biotechnology

Transform Omics Data into Precision Biotechnology Insights with AI

Skills you will gain:

About Workshop:

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.

Workshop 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: Omics Data and AI Workflow Foundations

  • Genomics, transcriptomics, proteomics, metabolomics
  • Data formats and biological meaning & Precision biotech use cases
  • Data collection, preprocessing & feature engineering & training and validation
  • Hands-on: Loading and cleaning omics datasets
  • Exploratory analysis and feature preparation
  • Tools: Google Colab/ Python/ Pandas/ NumPy/ Scanpy/ BioPython/ Matplotlib

Day 2: Machine Learning for Omics Analysis

  • Classification and regression tasks
  • Biomarker discovery & Patient/sample stratification
  • Multimodal and Integrative Analysis
  • Combining multiple omics layers
  • Dimensionality reduction
  • Clustering and representation learning
  • Hands-on: Train a model on omics data
  • Perform PCA/UMAP and clustering
  • Compare model performance
  • Tools: Scikit-learn, XGBoost, Scanpy, UMAP & TensorFlow or PyTorch

Day 3: End-to-End Precision Biotechnology Pipeline

  • Feature importance, model interpretation
  • translating predictions into biology
  • Deploying a Practical Omics Pipeline
  • Workflow automation, reproducibility & reporting results
  • Hands-on: Build a mini omics AI pipeline from raw input to biological insight dashboard
  • Tools: Google Colab / Jupyter/ SHAP/ Streamlit/ GitHub/ CSV/Excel integration tools

Mentor Profile

Fee Plan

StudentINR 2499/- OR USD 75
Ph.D. Scholar / ResearcherINR 3499/- OR USD 85
Academician / FacultyINR 4499/- OR USD 95
Industry ProfessionalINR 5499/- OR USD 105

Important Dates

Registration Ends
20 Apr 2026 Indian Standard Timing 7:00 PM IST
Workshop Dates
20 Apr 2026 to
22 Apr 2026  Indian Standard Timing 8:00 PM IST

Get an e-Certificate of Participation!

2024Certfiacte

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

Omics Preprocessing Integration Prediction Validation Biomarkers Python

Workshop 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.