Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on MultiPlex Biological Networks.

Jiacheng Liu, Liu Zhu, Dan Cao, Xinghui Zhu, Hongyan Zhang, Yinqiong Zhang, Jing Liu

Abstract read

In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Predicting rice drought-responsive genes via distance-based prototypical graph neural network with path aggregation mechanism.Plant methods · 2026
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Authors and funding

7 authors.

Jiacheng LiuCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

Liu ZhuCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.ORCID 0000-0002-9582-4541

Dan CaoCollege of Science, Central South University of Forestry and Technology, Changsha 410004, China.

Xinghui ZhuCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

Hongyan ZhangCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

Yinqiong ZhangCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

Jing LiuCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

Funding

Natural Science Foundation of Hunan Province 2021JJ30351Scientific Research Foundation of Education Office of Hunan Province 21C0133

6 · The paper itself

Abstract

Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysis and biological molecular network analysis provide robust solutions. This study proposed a random walk with a multi-restart probability (RWMRP) algorithm, based on the Restarted Random Walk (RWR) algorithm, to operate on rice MultiPlex biological networks. It explores the interactions between biological molecules across various levels and ranks potential genes. RWMRP uses eigenvector centrality to evaluate node importance in the network and adjusts the restart probabilities accordingly, diverging from the uniform restart probability employed in RWR. In the random walk process, it can be better to consider the global relationships in the network. Firstly, we constructed a MultiPlex biological network by integrating the rice protein-protein interaction, gene pathway, and gene co-expression network. Then, we employed RWMRP to predict the potential genes associated with rice tolerance to drought stress. Enrichment and correlation analyses resulted in the identification of 12 drought-related genes. We further conducted quantitative real-time polymerase chain reaction (qRT-PCR) analysis on these 12 genes, ultimately identifying 10 genes responsive to drought stress.

Indexed as

AlgorithmsDroughtsGene Expression Regulation, PlantGene Regulatory NetworksOryzaStress, PhysiologicalGene Expression ProfilingGenes, PlantPlant ProteinsProtein Interaction MapsPlant Proteinsdrought stress-responsive geneseigenvector centralityMultiPlex biological networksrandom walk with multi-restart probabilityrice

Identifiers

PMID39273165

DOI10.3390/ijms25179216

PMCPMC11395135

What Socratic holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.