Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference.

ABSTRACT: Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were ap

SUBMITTER: Aziz F 

PROVIDER: S-EPMC7660606 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature