A Directed Acyclic Graphical Approach and Ensemble Feature Selection for a Better Drug Development Strategy Using Partial Knowledge from KEGG Signalling Pathways

Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study

“…In fact, the AIC was shown to give more reliable results compared with alternative methods such as leave-one-out crossvalidation method (LOOCV) especially in gene expression data sets as well as in big data sets. 11 The AIC score function in that form focuses on finding best-fit parents, which does not necessarily incur concurrent best estimate of the regressive parameters ( β s), namely, model selection. To alleviate this concern, the lasso estimate 23 , 24 was used alongside the AIC such as to ensure that the sum of the absolute values of the model regressive parameters ( β s) is below a prespecified threshold parameter ( s ) using the following penalty function:…”

“…Interestingly, incorporation of biological prior knowledge in machine learning of graphical models has shown the prospects of efficient learning of gene-gene regulatory networks such as to overcome problems incurred by sparse gene expression data. 11 – 14 In this context, incorporation of biological prior knowledge amounts to supplementing the graphical model technique with available information about gene expression data using cell signaling pathways relevant to the problem under investigation. This leads to restricting the variable space to lower dimensions and thereby circumventing the overfitting problems incurred in dealing with sparse gene expression profile data.…”