Skills you will gain:
About Program:
The growing volume of omics data—generated from next-generation sequencing, mass spectrometry, and other high-throughput platforms—presents significant challenges for analysis and interpretation. Advanced AI techniques such as machine learning, deep learning, and natural language processing (NLP) can uncover hidden patterns and predictive biomarkers, while high-performance computing (HPC) allows for the efficient processing of these large, complex datasets. This workshop will cover how AI models can be applied to omics data analysis, such as gene expression profiling, protein-protein interactions, and genomic variations, and how HPC can accelerate data processing and analysis.
Aim: The growing volume of omics data—generated from next-generation sequencing, mass spectrometry, and other high-throughput platforms—presents significant challenges for analysis and interpretation. Advanced AI techniques such as machine learning, deep learning, and natural language processing (NLP) can uncover hidden patterns and predictive biomarkers, while high-performance computing (HPC) allows for the efficient processing of these large, complex datasets. This workshop will cover how AI models can be applied to omics data analysis, such as gene expression profiling, protein-protein interactions, and genomic variations, and how HPC can accelerate data processing and analysis.
Program Objectives:
- Understand the fundamentals of AI and HPC in the context of omics and biomedical data.
- Learn to apply machine learning and deep learning models to analyze genomic, transcriptomic, proteomic, and metabolomic data.
- Explore HPC infrastructures and tools for large-scale data processing and analysis.
- Gain experience in using AI-based and HPC-enabled platforms for biomarker discovery and disease modeling.
- Apply AI and HPC workflows to real-world biomedical research problems in precision medicine and clinical genomics.
What you will learn?
Day 1: Foundations of Omics + HPC Systems
- Normalization techniques (TPM, FPKM)
- Handling missing data, Feature scaling & Dimensionality reduction
- PCA, variance filtering, Neural networks in genomics
- Autoencoders & CNN basics
- Job scheduling (SLURM basics)
- Why HPC is essential for omics, Connecting to HPC (SSH)
- Basic Linux commands
- File handling and directory setup
- Writing SLURM scripts
Day 2: AI/ML for Omics Data
- Dimensionality reduction
- PCA, variance filtering
- Random Forest, SVM, K-means
- Use cases: disease classification, biomarker discovery
- Hands-on: Load gene expression dataset, Train classification model & Interpret results
- Hands-on: Running a basic RNA-seq pipeline & Monitoring performance on HPC
- Hands-on: End-to-End AI Case Study, Data loading, Preprocessing, Model training & Evaluation
- Hands-on: Pipeline Execution Running a basic RNA-seq pipeline
- Hands-on: Monitoring performance on HPC
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program Outcomes
- Apply AI models and HPC infrastructures to analyze complex omics and biomedical datasets.
- Gain expertise in data-driven discovery and biomarker identification using AI and HPC.
- Develop machine learning models for genomic data analysis, protein interaction, and disease prediction.
- Understand the role of HPC in scaling computational workflows for large datasets.
- Design and implement AI-driven pipelines for precision medicine and clinical genomics.