Open AccessBiological Network, Gene Regulatory Network Inference Using Causal Inference Approach
Author(s) -
Saroj Shambharkar,
K. Vaishali,
Rachna Somkunwar,
Yogeshri Choudhari,
Jyotsna Gawai
Publication year - 2022
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.360116
Subject(s) - inference , gene regulatory network , causal inference , computer science , granger causality , artificial intelligence , cluster analysis , computational biology , causality (physics) , machine learning , data mining , gene , biology , mathematics , econometrics , gene expression , genetics , physics , quantum mechanics
In system biology inference from gene regulatory network (GRN) is a challenging task. There exist different computational techniques to analyze the causal relationships between the pair of genes and to understand the significance of causal relationship in gene regulatory network. The DREAM4 insilico network structure and insilico gene expression time series dataset of DREAM challenge dataset is examined. This gene expression dataset of insilico of size 10 is analyzed for inferring causal relationships of the GRN inference. The analysis of dataset showing the gene expression data values are varying with respect to time. The paper focused on the different models of causal inference approach, Genetic Algorithm framework for the GRN inference. In this dataset, values associated with genes are analyzed using the Granger causality test and clustering to analyze the correlation and interaction or causal relationships among genes. The objective behind analysis and inferring causal information in the GRN is to reveal the study on gene activities to achieve more biological insights.