Incorporating Pathway Information into Feature Selection Towards Better Performed Gene Signatures

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable

Enhanced directed random walk for the identification of breast cancer prognostic markers from multiclass expression data

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 expres-sions. 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 sub-types 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. There-fore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes

Identification of pathway and gene markers using enhanced directed random walk for multiclass cancer expression data

Cancer markers play a significant role in the diagnosis of the origin of cancers and in the detection of cancers from initial treatments. This is a challenging task owing to the heterogeneity nature of cancers. Identification of these markers could help in improving the survival rate of cancer patients, in which dedicated treatment can be provided according to the diagnosis or even prevention. Previous investigations show that the use of pathway topology information could help in the detection of cancer markers from gene expression. Such analysis reduces its complexity from thousands of genes to a few hundreds of pathways. However, most of the existing methods group different cancer subtypes into just disease samples, and consider all pathways contribute equally in the analysis process. Meanwhile, the interaction between multiple genes and the genes with missing edges has been ignored in several other methods, and hence could lead to the poor performance of the identification of cancer markers from gene expression. Thus, this research proposes enhanced directed random walk to identify pathway and gene markers for multiclass cancer gene expression data. Firstly, an improved pathway selection with analysis of variances (ANOVA) that enables the consideration of multiple cancer subtypes is performed, and subsequently the integration of k-mean clustering and average silhouette method in the directed random walk that considers the interaction of multiple genes is also conducted. The proposed methods are tested on benchmark gene expression datasets (breast, lung, and skin cancers) and biological pathways. The performance of the proposed methods is then measured and compared in terms of classification accuracy and area under the receiver operating characteristics curve (AUC). The results indicate that the proposed methods are able to identify a list of pathway and gene markers from the datasets with better classification accuracy and AUC. The proposed methods have improved the classification performance in the range of between 1% and 35% compared with existing methods. Cell cycle and p53 signaling pathway were found significantly associated with breast, lung, and skin cancers, while the cell cycle was highly enriched with squamous cell carcinoma and adenocarcinoma