Introduction Women’s health is a critical pillar of global public health, encompassing physical, mental, and reproductive well-being across all stages of life. Due to a combination of biological, social, and cultural factors, women are disproportionately affected by certain health conditions [1]. According to the World Health Organization (WHO), over 35 million new cancer cases are

Approaches for accelerating microbial gene function discovery using artificial intelligence – Nature Microbiology
Hutchison, C. A. I. et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253 (2016).
Article PubMed Google Scholar
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Article CAS PubMed PubMed Central Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Article CAS PubMed PubMed Central Google Scholar
Lim, Y. et al. In silico protein interaction screening uncovers DONSON’s role in replication initiation. Science 381, eadi3448 (2023).
Article CAS PubMed PubMed Central Google Scholar
Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).
Article CAS PubMed PubMed Central Google Scholar
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: exploring the boundaries of protein language models. Cell Syst. 14, 968–978.e3 (2023).
Article CAS PubMed Google Scholar
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Article CAS PubMed PubMed Central Google Scholar
Rhee, H. S. & Pugh, B. F. ChIP-exo method for identifying genomic location of DNA-binding proteins with near-single-nucleotide accuracy. Curr. Protoc. Mol. Biol. 100, 21.24.1–21.24.14 (2012).
Article Google Scholar
Gao, Y. et al. Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo. Nucleic Acids Res. 49, 9696–9710 (2021).
Article CAS PubMed PubMed Central Google Scholar
Kim, G. B., Gao, Y., Palsson, B. O. & Lee, S. Y. DeepTFactor: a deep learning-based tool for the prediction of transcription factors. Proc. Natl Acad. Sci. USA 118, e2021171118 (2021).
Article CAS PubMed Google Scholar
Gao, Y. et al. Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655. Nucleic Acids Res. 46, 10682–10696 (2018).
Article CAS PubMed PubMed Central Google Scholar
Perez-Rueda, E. & Collado-Vides, J. The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12. Nucleic Acids Res. 28, 1838–1847 (2000).
Article CAS PubMed PubMed Central Google Scholar
Mejia-Almonte, C. et al. Redefining fundamental concepts of transcription initiation in bacteria. Nat. Rev. Genet. 21, 699–714 (2020).
Article CAS PubMed PubMed Central Google Scholar
Ishihama, A., Shimada, T. & Yamazaki, Y. Transcription profile of Escherichia coli: genomic SELEX search for regulatory targets of transcription factors. Nucleic Acids Res. 44, 2058–2074 (2016).
Article CAS PubMed PubMed Central Google Scholar
Sastry, A. V. et al. The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat. Commun. 10, 5536 (2019).
Article CAS PubMed PubMed Central Google Scholar
Rodionova, I. A. et al. Identification of a transcription factor, PunR, that regulates the purine and purine nucleoside transporter punC in E. coli. Commun. Biol. 4, 991 (2021).
Article CAS PubMed PubMed Central Google Scholar
Poudel, S. et al. Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators, and role in key physiological response. Proc. Natl Acad. Sci. USA 117, 17228–17239 (2020).
Article CAS PubMed PubMed Central Google Scholar
Miller, H. K. et al. The extracytoplasmic function sigma factor σS protects against both intracellular and extracytoplasmic stresses in Staphylococcus aureus. J. Bacteriol. 194, 4342–4354 (2012).
Article CAS PubMed PubMed Central Google Scholar
Catoiu, E. A. et al. iModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets. Nucleic Acids Res. 53, D99–D106 (2025).
Article PubMed Google Scholar
Yu, C., Zavaljevski, N., Desai, V. & Reifman, J. Genome-wide enzyme annotation with precision control: catalytic families (CatFam) databases. Proteins 74, 449–460 (2009).
Article CAS PubMed Google Scholar
Desai, D. K., Nandi, S., Srivastava, P. K. & Lynn, A. M. ModEnzA: accurate identification of metabolic enzymes using function specific profile HMMs with optimised discrimination threshold and modified emission probabilities. Adv. Bioinform 2011, 743782 (2011).
Article Google Scholar
Claudel-Renard, C., Chevalet, C., Faraut, T. & Kahn, D. Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res. 31, 6633–6639 (2003).
Article CAS PubMed PubMed Central Google Scholar
Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proc. Natl Acad. Sci. USA 116, 13996–14001 (2019).
Article CAS PubMed PubMed Central Google Scholar
Kim, G. B. et al. Functional annotation of enzyme-encoding genes using deep learning with transformer layers. Nat. Commun. 14, 7370 (2023).
Article CAS PubMed PubMed Central Google Scholar
Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Nielsen, H. & Winther, O. DeepLoc 2.0: multi-label subcellular localization prediction using protein language models. Nucleic Acids Res. 50, W228–W234 (2022).
Article CAS PubMed PubMed Central Google Scholar
Yu, T. et al. Enzyme function prediction using contrastive learning. Science 379, 1358–1363 (2023).
Article CAS PubMed Google Scholar
Zhang, C., Freddolino, L. & Zhang, Y. COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information. Nucleic Acids Res. 45, W291–W299 (2017).
Article CAS PubMed PubMed Central Google Scholar
Sanderson, T., Bileschi, M. L., Belanger, D. & Colwell, L. J. ProteInfer, deep neural networks for protein functional inference. eLife 12, e80942 (2023).
Article CAS PubMed PubMed Central Google Scholar
Wang, T. et al. Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform. Sci. Adv. 11, eadt0402 (2025).
Article CAS PubMed PubMed Central Google Scholar
Mateus, A. et al. The functional proteome landscape of Escherichia coli. Nature 588, 473–478 (2020).
Article CAS PubMed PubMed Central Google Scholar
Kulmanov, M., Khan, M. A., Hoehndorf, R. & Wren, J. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics 34, 660–668 (2018).
Article CAS PubMed Google Scholar
Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40, 932–937 (2022).
Article CAS PubMed Google Scholar
Abdin, O., Nim, S., Wen, H. & Kim, P. M. PepNN: a deep attention model for the identification of peptide binding sites. Commun. Biol. 5, 503 (2022).
Article CAS PubMed PubMed Central Google Scholar
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Article CAS PubMed Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Article CAS PubMed PubMed Central Google Scholar
Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594–602 (2023).
Article CAS PubMed PubMed Central Google Scholar
Barrio-Hernandez, I. et al. Clustering predicted structures at the scale of the known protein universe. Nature 622, 637–645 (2023).
Article CAS PubMed PubMed Central Google Scholar
Dalkiran, A. et al. ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature. BMC Bioinform. 19, 334 (2018).
Article CAS Google Scholar
Shi, Z. et al. Enzyme Commission number prediction and benchmarking with hierarchical dual-core multitask learning framework. Research 6, 0153 (2023).
Article CAS PubMed PubMed Central Google Scholar
Nguyen, T. B., de Sá, A. G. C., Rodrigues, C. H. M., Pires, D. E. V. & Ascher, D. B. LEGO-CSM: a tool for functional characterization of proteins. Bioinformatics 39, btad402 (2023).
Article CAS PubMed PubMed Central Google Scholar
Buton, N., Coste, F. & Le Cunff, Y. Predicting enzymatic function of protein sequences with attention. Bioinformatics 39, btad620 (2023).
Article CAS PubMed PubMed Central Google Scholar
Han, S. R. et al. Evidential deep learning for trustworthy prediction of Enzyme Commission number. Brief. Bioinform. 25, bbad401 (2023).
Article PubMed PubMed Central Google Scholar
Watanabe, N., Yamamoto, M., Murata, M., Kuriya, Y. & Araki, M. EnzymeNet: residual neural networks model for Enzyme Commission number prediction. Bioinform. Adv. 3, vbad173 (2023).
Article PubMed PubMed Central Google Scholar
