Abstract
Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.
Highlights
Cancer is associated with abnormal alterations that lead to the dysregulation of the cellular system [1]
An improved weight strategy using one-way analysis of variance (ANOVA) (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+
The results show that using the logistic regression model and Naïve Bayes in eDRW+ provided the highest AUC values in GSE1456 compared with the other methods
Summary
Cancer is associated with abnormal alterations that lead to the dysregulation of the cellular system [1]. Breast cancer is the most common cancer found in women worldwide [2]. Luminal A, luminal B, basal, ERBB2, and normal-like are the five molecular subtypes of breast cancer from gene expression profiling. The accurate classification of diseases and treatment responses is helpful in clinical and cancer biology research [1,3,4]. The classification aims to identify patients with similar clinical features (characteristics) in order to identify and implement suitable treatments [5]. Pathway-based microarray analysis reduces its complexity of analysis from thousands of genes to a few hundred pathways [6]
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