Using ILP to Identify Pathway Activation Patterns in Systems Biology

Sam Neaves, Louise Millard, Sophia Tsoka

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
270 Downloads (Pure)

Abstract

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist.
Original languageEnglish
Title of host publicationInductive Logic Programming
Subtitle of host publication25th International Conference, ILP 2015, Kyoto, Japan, August 20-22, 2015, Revised Selected Papers
EditorsKatsumi Inoue, Hayato Ohwada, Akihiro Yamamoto
PublisherSpringer Verlag
Pages137-151
Number of pages15
ISBN (Electronic)9783319405667
ISBN (Print)9783319405650
DOIs
Publication statusPublished - 10 Jun 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9575
ISSN (Print)0302-9743

Keywords

  • Biological pathways
  • Warmr
  • TreeLiker
  • Reactome
  • Barcode
  • Logical aggregation

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