Network Inference from Omics Data — Hands-on

Learn practical workflows for reconstructing biological interaction networks directly from omics datasets. This module covers correlation and mutual information based methods, regression and tree based approaches, basic graphical models and good practices for validation, benchmarking and interpretation.

Network Inference from Omics Data

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Session Index

Session 1 — Omics Data & Correlation Networks Session 2 — Information Theoretic & Regression Methods Session 3 — Advanced Inference & Benchmarking Session 4 — Mini Capstone: End to End Network Inference

Session 1

Fee: Rs 8800

Omics Data & Correlation Networks

Omics matrices and preprocessing for inference

expression matrices normalization and filtering batch effects overview

Correlation based network construction

Pearson and Spearman distance correlation basics thresholding strategies

Weighted co expression networks

soft thresholds co expression adjacency brief WGCNA concepts

Session 2

Fee: Rs 11800

Information Theoretic & Regression Methods

Mutual information based inference

mutual information concepts ARACNe style pruning relevance networks

Regression and sparse models for edges

Lasso and Elastic Net neighbourhood selection regularization and sparsity

Tree based and ensemble methods

GENIE3 style approaches Random Forest importance variable selection view

Session 3

Fee: Rs 14800

Advanced Inference & Benchmarking

Partial correlations and graphical models

Gaussian graphical models precision matrix view conditional independence

Time series and perturbation data

time lagged networks dynamic Bayesian ideas using knockdown data

Evaluation and benchmarking of inferred networks

gold standards and prior knowledge precision recall style metrics robustness checks

Session 4

Fee: Rs 18800

Mini Capstone: End to End Network Inference

Select an omics dataset and define the inference goal

Theory plus guided practical

Implement at least two inference strategies and compare

correlation vs MI or regression Python or R implementation basic sensitivity analysis

Deliverables: scripts, network files and short report

code notebook edge list or GraphML PDF or HTML summary

PDF

Network Inference from Omics Data Training | Co expression, MI and Graphical Models

Network Inference from Omics Data — Hands-on