Agari Y, Kuramitsu S, Shinkai A (2010) Identification of novel genes regulated by the oxidative stress-responsive transcriptional activator SdrP in thermus thermophilus HB8. FEMS Microbiol Lett 313:127–134

Article  CAS  PubMed  Google Scholar 

Ahsen ME (2025) Harnessing unsupervised ensemble learning for biomedical applications: A review of methods and advances. Mathematics 13:420

Article  Google Scholar 

Apel K, Hirt H (2004) Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu Rev Plant Biol 55:373–399

Article  CAS  PubMed  Google Scholar 

Baldi P, Long AD (2001) A bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17:509–519

Article  CAS  PubMed  Google Scholar 

Bonilla CY (2020) Generally stressed out bacteria: environmental stress response mechanisms in gram-positive bacteria. Integr Comp Biol 60:126–133

Article  CAS  PubMed  Google Scholar 

Bowler S, Papoutsoglou G, Karanikas A, Tsamardinos I, Corley MJ, Ndhlovu LC (2022) A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity. Sci Rep 12:17480

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cai J, Lee Z-J, Lin Z, Hsu C-H, Lin Y (2024) An Integrated Algorithm with Feature Selection, Data Augmentation, and XGBoost for Ovarian Cancer. Mathematics (2227–7390) 12

Cheng Z, Zhang J, Ballou DP, Williams CH Jr (2011) Reactivity of thioredoxin as a protein thiol-disulfide oxidoreductase. Chem Rev 111:5768–5783

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dawan J, Ahn J (2022) Bacterial stress responses as potential targets in overcoming antibiotic resistance. Microorganisms 10:1385

Article  CAS  PubMed  PubMed Central  Google Scholar 

Elleuche S, Schäfers C, Blank S, Schröder C, Antranikian G (2015) Exploration of extremophiles for high temperature biotechnological processes. Curr Opin Microbiol 25:113–119

Article  CAS  PubMed  Google Scholar 

Galal A, Talal M, Moustafa A (2022) Applications of machine learning in metabolomics: disease modeling and classification. Front Genet 13:1017340

Article  PubMed  PubMed Central  Google Scholar 

Głuchowska A, Zieniuk B, Pawełkowicz M (2025) Unlocking plant resilience: metabolomic insights into abiotic stress tolerance in crops. Metabolites 15:384

Article  PubMed  PubMed Central  Google Scholar 

Guan N, Li J, Shin H-d, Du G, Chen J, Liu L (2017) Microbial response to environmental stresses: from fundamental mechanisms to practical applications. Appl Microbiol Biotechnol 101:3991–4008

Article  CAS  PubMed  Google Scholar 

Haimlich S, Fridman Y, Khandal H, Savaldi-Goldstein S, Levy A (2024) Widespread horizontal gene transfer between plants and bacteria. ISME Commun 4:ycae073

Article  PubMed  PubMed Central  Google Scholar 

Hidalgo A, Betancor L, Moreno R, Zafra O, Cava F, Fernández-Lafuente R, Guisán JM, Berenguer J (2004) Thermus thermophilus as a cell factory for the production of a thermophilic Mn-dependent catalase which fails to be synthesized in an active form in Escherichia coli. Appl Environ Microbiol 70:3839–3844

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ho YSJ, Burden LM, Hurley JH (2000) Structure of the GAF domain, a ubiquitous signaling motif and a new class of Cyclic GMP receptor. The EMBO journal

Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264

Article  PubMed  Google Scholar 

Jawaharraj K, Peta V, Dhiman SS, Gnimpieba EZ, Gadhamshetty V (2023) Transcriptome-wide marker gene expression analysis of stress-responsive sulfate-reducing bacteria. Sci Rep 13:16181

Article  CAS  PubMed  PubMed Central  Google Scholar 

Karimi-Fard A, Saidi A, Tohidfar M, Saxena A (2023) Identification of key responsive genes to some abiotic stresses in Arabidopsis Thaliana at the seedling stage based on coupling computational biology methods and machine learning. J Appl Biotechnol Rep 10:1079–1090

CAS  Google Scholar 

Karimi-Fard A, Saidi A, TohidFar M, Emami SN (2024) Novel candidate genes for environmental stresses response in synechocystis sp. PCC 6803 revealed by machine learning algorithms. Brazilian J Microbiol 55:1219–1229

Article  CAS  Google Scholar 

Karimi-Fard A, Saidi A, Tohidfar M, Emami SN (2025) Integrative bioinformatics approaches reveal key hub genes in cyanobacteria: insights from synechocystis sp. PCC 6803 and geminocystis sp. NIES-3708 under abiotic stress conditions. Genes Genomics :1–15

Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD (2012) The Sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882–883

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li Z, Gao E, Zhou J, Han W, Xu X, Gao X (2023) Applications of deep learning in understanding gene regulation. Cell Reports Methods 3

Liang Y, Zhang F, Wang J, Joshi T, Wang Y, Xu D (2011) Prediction of drought-resistant genes in Arabidopsis Thaliana using SVM-RFE. PLoS ONE 6:e21750

Article  CAS  PubMed  PubMed Central  Google Scholar 

Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2 – ∆∆CT method. Methods 25:402–408

Article  CAS  PubMed  Google Scholar 

Loureiro A, da Silva GJ (2019) CRISPR-Cas: converting a bacterial defence mechanism into a state-of-the-art genetic manipulation tool. Antibiotics 8:18

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ma C, Xin M, Feldmann KA, Wang X (2014) Machine learning–based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis. Plant Cell 26:520–537

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mienye ID, Sun Y (2022) A survey of ensemble learning: concepts, algorithms, applications, and prospects. Ieee Access 10:99129–99149

Article  Google Scholar 

Oshima T, Imahori K (1974) Description of thermus thermophilus (Yoshida and Oshima) comb. Nov., a nonsporulating thermophilic bacterium from a Japanese thermal spa. Int J Syst Evol MicroBiol 24:102–112

CAS  Google Scholar 

Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM (2022) A review of feature selection methods for machine learning-based disease risk prediction. Front Bioinf 2:927312

Article  Google Scholar 

Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 5:e184

Article  PubMed  PubMed Central  Google Scholar 

Rao MS, Van Vleet TR, Ciurlionis R, Buck WR, Mittelstadt SW, Blomme EA, Liguori MJ (2019) Comparison of RNA-Seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies. Front Genet 9:636

Article  PubMed  PubMed Central  Google Scholar 

Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare RP, Banday S, Mishra AK, Das G (2023) Next-generation sequencing technology: current trends and advancements. Biology 12:997

Article  CAS  PubMed  PubMed Central  Google Scholar 

Teague JL, Barrows JK, Baafi CA, Van Dyke MW (2021) Discovering the DNA-binding consensus of the thermus thermophilus HB8 transcriptional regulator TTHA1359. Int J Mol Sci 22:10042

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vizza P, Aracri F, Guzzi PH, Gaspari M, Veltri P, Tradigo G (2024) Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case. BMC Med Inf Decis Mak 24:93

Article