Neaves, Samuel ![]() |
Official URL: https://doi.org/10.1007/978-3-319-40566-7_10
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.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Published Online |
Status: | Published |
Schools: | Medicine |
Publisher: | Springer |
ISBN: | 9783319405650 |
Last Modified: | 19 Nov 2024 16:01 |
URI: | https://orca.cardiff.ac.uk/id/eprint/172256 |
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