BSBT4002 · workspace

Big Data and Biological Networks

Syllabus, study tools, lectures, and curriculum map.

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction to Biological Big Data. Information Flow in Biological Systems.

Incomplete

02W02

Omics datasets: Various flavours of big biological datasets (genomic, transcriptomic, proteomic, metabolomic, etc.).

Incomplete

03W03

Introduction to Graph theory. History. Types of graphs. Representing biological networks.

Incomplete

04W04

Network structure: Key parameters, measures of centrality

Incomplete

05W05

Key Network Models: Erdos-Renyi, Watts-Strogatz (small-world) and Barabasi-Albert (power-law models)

Incomplete

06W06

Network clustering/community detection. Identifying motifs in networks. Studying network perturbations.

Incomplete

07W07

Applications of network biology: Predicting drug targets, predicting drug molecules, synthesis of new molecules (chemoinformatics)

Incomplete

08W08

Applications of network biology: Epidemiology, Centrality-lethality hypothesis.

Incomplete

09W09

AI & ML for Biological Data Analysis. Introduction to AI & ML tasks in biological networks.

Incomplete

010W10

Biological network reconstruction from omics and literature data

Incomplete

011W11

Property prediction using network data. Node classification and link prediction.

Incomplete

012W12

Analysis of heterogeneous and multi-layer/multiplex networks. Future Perspectives.

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

Learning Objectives

Open this first for the core logic and traps.

Dive in

1 min

Learning Objectives

Open this first for the core logic and traps.

Dive in

1 min

Learning Objectives

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSBT4002

BS Degree

4 Credits

Big Data and Biological Networks

To enable the students to “understand” biological data, to represent, and analyze various datasets from a network perspective, to encourage networ...

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction to Biological Big Data. Information Flow in Biological Systems.

Module 2

Omics datasets: Various flavours of big biological datasets (genomic, transcriptomic, proteomic, metabolomic, etc.).

Module 3

Introduction to Graph theory. History. Types of graphs. Representing biological networks.

Module 4

Network structure: Key parameters, measures of centrality

Module 5

Key Network Models: Erdos-Renyi, Watts-Strogatz (small-world) and Barabasi-Albert (power-law models)

Module 6

Network clustering/community detection. Identifying motifs in networks. Studying network perturbations.

Module 7

Applications of network biology: Predicting drug targets, predicting drug molecules, synthesis of new molecules (chemoinformatics)

Module 8

Applications of network biology: Epidemiology, Centrality-lethality hypothesis.

Module 9

AI & ML for Biological Data Analysis. Introduction to AI & ML tasks in biological networks.

Module 10

Biological network reconstruction from omics and literature data

Module 11

Property prediction using network data. Node classification and link prediction.

Module 12

Syllabus

Topic

Introduction to Biological Big Data. Information Flow in Biological Systems.

Omics datasets: Various flavours of big biological datasets (genomic, transcriptomic, proteomic, metabolomic, etc.).

Introduction to Graph theory. History. Types of graphs. Representing biological networks.

Network structure: Key parameters, measures of centrality

Key Network Models: Erdos-Renyi, Watts-Strogatz (small-world) and Barabasi-Albert (power-law models)

Network clustering/community detection. Identifying motifs in networks. Studying network perturbations.

Applications of network biology: Predicting drug targets, predicting drug molecules, synthesis of new molecules (chemoinformatics)

Applications of network biology: Epidemiology, Centrality-lethality hypothesis.

AI & ML for Biological Data Analysis. Introduction to AI & ML tasks in biological networks.

Biological network reconstruction from omics and literature data

Property prediction using network data. Node classification and link prediction.

Analysis of heterogeneous and multi-layer/multiplex networks. Future Perspectives.

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Big Data and Biological Networks

Execution Protocol

Topic

Introduction to Biological Big Data. Information Flow in Biological Systems.

Omics datasets: Various flavours of big biological datasets (genomic, transcriptomic, proteomic, metabolomic, etc.).

Introduction to Graph theory. History. Types of graphs. Representing biological networks.

Network structure: Key parameters, measures of centrality

Key Network Models: Erdos-Renyi, Watts-Strogatz (small-world) and Barabasi-Albert (power-law models)

Network clustering/community detection. Identifying motifs in networks. Studying network perturbations.

Applications of network biology: Predicting drug targets, predicting drug molecules, synthesis of new molecules (chemoinformatics)

Applications of network biology: Epidemiology, Centrality-lethality hypothesis.

AI & ML for Biological Data Analysis. Introduction to AI & ML tasks in biological networks.

Biological network reconstruction from omics and literature data

Property prediction using network data. Node classification and link prediction.

Analysis of heterogeneous and multi-layer/multiplex networks. Future Perspectives.

Video Archive

Intelligence Hub