A novel method of gene regulatory network structure inference from gene knock-out expression data

Abstract

Inferring Gene Regulatory Networks (GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theorybased network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.

A data-driven clustering recommendation method for single-cell RNA-sequencing data

“…However, noise in gene expressions may affect the accuracy of the data latent shapes identification. In the future, we can introduce some prior biological information such as marker genes and gene regulatory relationship [74,75] to assist in a more accurate extraction of informative features from scRNA-seq data under different biological backgrounds. Furthermore, the increasing scale of scRNA-seq data brings a challenge to the efficiency of current methods, and approaches such as data partitioning or sampling [76,77] may provide a possible way to solve this problem.…”

APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

“…Gene expression data is another category of process information providing a gene to regulate the synthesis of a functional gene product, which are mostly proteins. 48 They are collectively available in GEO database. Mostly, they are presented in two ways, namely microarray and RNA-seq data.…”

“…Entitled with the three important properties, each single protein has its own GO terms. Gene expression data is another category of process information providing a gene to regulate the synthesis of a functional gene product, which are mostly proteins 48 . They are collectively available in GEO database.…”

Novel modelling of clustering for enhanced classification performance on gene expression data

“…-The background There have been various approaches towards enhancing the clustering performance over the medical data [10]. The recent work carried out by Chen et al [11] has introduced a network model for analyzing the redundant informationin the gene expression data. Identification of the specific form of cancer was carried out by Farouq et al [12] over gene expression data.…”