SynthesisBiology direct2026
Harnessing meta-analysis and artificial intelligence to reveal conserved regulatory biosignatures of abiotic stress in soybean.
Synthesis in Biology direct, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSoybeans are widely cultivated worldwide as an important source of edible vegetable oil and protein. Due to climate change, it is repeatedly exposed to various abiotic stressors in its natural habitat. Abiotic stresses such as heat, drought, and salinity severely restrict soybean productivity, yet the conserved molecular mechanisms underlying multi-stress tolerance remain poorly understood. The integrated application of machine learning and co-expression network analysis for robust biosignature and hub gene discovery remains limited. Therefore, this study aimed to identify conserved stress-responsive biosignatures and explore their evolutionary and regulatory significance.
resultsHere, we explored the transcriptional regulation of soybean under multiple abiotic stress conditions, including heat, drought, and salt. A total of 14,503 genes are differentially expressed across three stress conditions, with 466 genes common to all three. Gene Ontology and KEGG pathway analyses indicated that the meta-DEGs primarily participate in oxidative stress, hormone signaling, and metabolic pathways. Segmental duplication is the key driving force of stress response gene expansion, and most of these expansions occurred through the recent whole-genome duplication (WGD) in soybean. The 12 abiotic stress-responsive biosignatures were identified using a wedge co-expression network and machine learning (ML)- based hub genes. A deep neural network (DNN) model was constructed to validate stress biosignatures, achieving 97.39% and 76.47% prediction accuracies on the test and external validation sets, respectively.
conclusionsOur findings revealed conserved stress-responsive genes, key regulatory hubs, and oxidative stress as a central molecular feature governing multi-stress adaptation. The integration of artificial intelligence enabled accurate validation of biosignatures, offering valuable insights into functional genomics and genomic-assisted breeding strategies. This study offers a strong foundation for AI applications in plant breeding and supplies valuable resources for soybean genetic improvement.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.