Yang, L;
Ainali, C;
Tsoka, S;
Papageorgiou, LG;
(2014)
Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.
BMC Bioinformatics
, 15
, Article 390. 10.1186/s12859-014-0390-2.
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Abstract
Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies.
| Type: | Article |
|---|---|
| Title: | Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s12859-014-0390-2 |
| Publisher version: | http://dx.doi.org/10.1186/s12859-014-0390-2 |
| Language: | English |
| Additional information: | © 2014 Yang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| Keywords: | Artificial Intelligence, Breast Neoplasms, Databases, Genetic, Disease, Female, Gene Expression Profiling, Humans, Lung Neoplasms, Male, Mathematical Computing, Models, Theoretical, Oligonucleotide Array Sequence Analysis, Prostatic Neoplasms, Psoriasis, Signal Transduction |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1471320 |
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