TY - JOUR JF - BMC Bioinformatics A1 - Yang, L A1 - Ainali, C A1 - Tsoka, S A1 - Papageorgiou, LG KW - Artificial Intelligence KW - Breast Neoplasms KW - Databases KW - Genetic KW - Disease KW - Female KW - Gene Expression Profiling KW - Humans KW - Lung Neoplasms KW - Male KW - Mathematical Computing KW - Models KW - Theoretical KW - Oligonucleotide Array Sequence Analysis KW - Prostatic Neoplasms KW - Psoriasis KW - Signal Transduction N2 - 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. ID - discovery1471320 UR - http://dx.doi.org/10.1186/s12859-014-0390-2 SN - 1471-2105 N1 - © 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. TI - Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. AV - public VL - 15 Y1 - 2014/12/05/ ER -