Big Data Mining in Plant Stress Resistance Evolution and Germplasm Innovation

Dear Colleagues,

This Topic welcomes the submission of interdisciplinary research at the intersection of AI, bioinformatics, big data mining, plant stress biology, evolutionary biology, and genetic breeding. Topics of interest include, but are not limited to, the following:

Development and optimization of AI, machine learning, and deep learning models, as well as advanced bioinformatics workflows, for the integration, mining, and functional interpretation of plant stress-related multi-omics data (including genomics, pan-genomics, transcriptomics, single-cell omics, epigenomics, proteomics, and metabolomics).

Big data-driven dissection of the adaptive evolution mechanisms of plant stress resistance, including natural selection signature analysis, domestication sweep identification, and evolutionary dynamics of stress-responsive gene families in model plants, crops, and forest trees.

Large-scale population genomics studies, including genome-wide association studies (GWASs), genomic selection, expression quantitative trait locus (eQTL) mapping, and pan-genome mining of novel stress-resistant alleles from wild germplasm resources.

Functional validation and in-depth molecular mechanism characterization of key stress-related genes, non-coding RNAs, and regulatory networks identified via big data mining, in response to both abiotic stresses (drought, salinity, extreme temperature, heavy metal toxicity, waterlogging, etc.) and biotic stresses (pathogen infection, pest infestation, etc.).

Innovation and application of big data and AI-enabled precision breeding technologies, including genome design breeding, molecular marker-assisted selection, and genomic prediction, for the development of stress-resilient crop and forest tree germplasms.

Cross-species comparative genomics and evolutionary analysis of plant stress adaptation strategies across herbaceous and woody plant species.

Manuscripts that are purely descriptive, lack innovative algorithm development or in-depth mechanistic insights, and do not align with the core theme of this Topic will not be considered for peer review.

Dr. Yan Cheng
Prof. Dr. Yuling Lin
Dr. Shijiang Cao
Topic Editors