Plant omics, which includes genomics, transcriptomics, metabolomics and proteomics, has played a remarkable role in the discovery of new genes and biomolecules that can be deployed for crop improvement. In wheat, great insights have been gleaned from the utilization of diverse omics approaches for both qualitative and quantitative traits. Especially, a combination of omics approaches has led to significant advances in gene discovery and pathway investigations and in deciphering the essential components of stress responses and yields. Recently, a Wheat Omics database has been developed for wheat which could be used by scientists for further accelerating functional genomics studies. In this review, we have discussed various omics technologies and platforms that have been used in wheat to enhance the understanding of the stress biology of the crop and the molecular mechanisms underlying stress tolerance.

Genomic prediction models are trained on single species and ignore temporal dynamics— assumptions that limit their biological scope. Here I present BioWorldModel , a unified architecture that predicts multi-trait phenotypic distributions across fungi, plants, and animals with a single set of parameters. The model introduces a scalable genotype encoder with organism-conditioned attention pooling, a four-channel biological memory system, and a Gaussian output head with diagonal variance parameterization. Trained jointly on five organisms spanning three kingdoms ( S. cerevisiae, A. thaliana, D. melanogaster, O. sativa, Z. mays ; 641 traits total), the model achieves organism-averaged R ² = 0.821 (trait-weighted R ² = 0.413) and substantially outperforms GBLUP, BayesB, Lasso, and Random Forest baselines trained per organism. These results demonstrate that genotype-to-phenotype mapping follows shared principles across kingdoms that a single model can exploit.

The same genome produces different phenotypes in different conditions—yet predictive models encode genotype once and treat each trait independently. Here we show that representing phenotype generation as a dynamic biological process transforms predictive accuracy across bacteria, fungi, animals and plants. BioWorldModel learns how organisms interpret their genome: frozen gene embeddings (species context) modulated by individual variation pass through four biological process layers (regulation → expression → pathway → cellular) that respond to environment and time. A state-conditioned attention mechanism rereads this dynamic representation, predicting full multivariate trait distributions. Without modification, the architecture achieves mean correlation r = 0.678 on 214 bacterial growth traits (207% better than ridge regression), r = 0.915 on 35 yeast fitness traits (167% better), r = 0.499 on 199 fly phenotypes in small-sample regime (760% better), and r = 0.995 on 36 rice traits (49% better). Ablations confirm that modeling biological process—not model size—drives performance. When neural architectures represent how biology generates phenotype rather than merely associating genotype with outcome, they capture what static methods miss.