Spring 2027

Computational Network Biology

Computational Network Biology introduces networks (graphs) for studying complex biological systems, surveying computational and graph-theoretic approaches for modeling, analysis, and discovery in biology and biomedicine.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2023: 3.64 GPA, 85.7% A/AB (n=21 letter grades); Fall 2024: 3.81 GPA, 95.2% A/AB (n=21 letter grades); Fall 2025: 3.75 GPA, 95.5% A/AB (n=22 letter grades).

What you’ll be able to do

  • Recognize problems in molecular biology that are appropriate for network modeling

  • Identify appropriate network algorithms for different tasks

  • Evaluate the strengths and weaknesses of different network algorithms designed for a specific biological problem

  • Gain practical experience in applying a select set of network algorithms on real data and evaluate its outputs

  • Understand the algorithmic and statistical concepts of different network-based approaches

Source: UW Guide 1 UW Guide 2

Grade history

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average GPA
letter grades
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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
BMI/COMPSCI 775

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Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Fall 2023: 3.64 GPA, 85.7% A/AB (n=21 letter grades); Fall 2024: 3.81 GPA, 95.2% A/AB (n=21 letter grades); Fall 2025: 3.75 GPA, 95.5% A/AB (n=22 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Networks and graphs for studying complex biological systems
  • Computational and graph-theoretic approaches using network algorithms
  • Hands-on implementation of computational projects in network biology

Skills

  • Selecting appropriate network algorithms for specific tasks
  • Evaluating strengths and weaknesses of network algorithms for biological problems
  • Applying network algorithms to real data and evaluating outputs
  • Understanding algorithmic and statistical concepts of network-based approaches

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "d5ab3b5069daa71f939ea33d7455cff730a9d174e345d5f5619321524d388232",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}