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Galaxy Tabular Learner: Building a Model using Chowell clinical data

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Abstract

What you will do

Questions this will address

  • How can Tabular Learner in Galaxy be used to reconstruct a LORIS-style (LLR6) logistic regression model using the same dataset and predictor set?
  • How should the decision threshold be configured (default vs selected cutoff) to align predictions with the intended clinical operating point?
  • Which components of the Tabular Learner report best support a transparent comparison to the published LORIS baseline?

Learning Objectives

  • Build an immunotherapy-response classifier in Galaxy using Tabular Learner.
  • Train and compare candidate models, then re-evaluate with a selected probability threshold.
  • Benchmark discrimination, calibration, and threshold-dependent metrics against the published LORIS LLR6 model.

Licence: Creative Commons Attribution 4.0 International

Keywords: Statistics and machine learning

Target audience: Students

Resource type: slides

Version: 2

Status: Active

Learning objectives:

  • Build an immunotherapy-response classifier in Galaxy using Tabular Learner.
  • Train and compare candidate models, then re-evaluate with a selected probability threshold.
  • Benchmark discrimination, calibration, and threshold-dependent metrics against the published LORIS LLR6 model.

Date modified: 2026-01-10

Date published: 2025-05-05

Authors: Jeremy Goecks, Junhao Qiu, Paulo Cilas Morais Lyra Junior

Contributors: Fabio Cumbo, Junhao Qiu, Paulo Cilas Morais Lyra Junior

Scientific topics: Statistics and probability

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