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
Ahsen ME (2025) Harnessing unsupervised ensemble learning for biomedical applications: A review of methods and advances. Mathematics 13:420
Apel K, Hirt H (2004) Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu Rev Plant Biol 55:373–399
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
Bonilla CY (2020) Generally stressed out bacteria: environmental stress response mechanisms in gram-positive bacteria. Integr Comp Biol 60:126–133
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
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
CAS PubMed PubMed Central Google Scholar
Dawan J, Ahn J (2022) Bacterial stress responses as potential targets in overcoming antibiotic resistance. Microorganisms 10:1385
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
Galal A, Talal M, Moustafa A (2022) Applications of machine learning in metabolomics: disease modeling and classification. Front Genet 13:1017340
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
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
Haimlich S, Fridman Y, Khandal H, Savaldi-Goldstein S, Levy A (2024) Widespread horizontal gene transfer between plants and bacteria. ISME Commun 4:ycae073
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Wang R (2012) AdaBoost for feature selection, classification and its relation with SVM, a review. Physics Procedia 25:800–807
Yang S, Kim JK (2020) Statistical data integration in survey sampling: A review. Japanese J Stat Data Sci 3:625–650
You X, Shu Y, Ni X, Lv H, Luo J, Tao J, Bai G, Feng S (2025) MLAS: machine Learning-Based approach for predicting abiotic Stress-Responsive genes in Chinese cabbage. Horticulturae 11:44
Zhang H, Zhang TT, Liu H, Shi DY, Wang M, Bie XM, Li XG, Zhang XS (2018) Thioredoxin-mediated ROS homeostasis explains natural variation in plant regeneration. Plant Physiol 176:2231–2250
CAS PubMed PubMed Central Google Scholar
Zhang X, Jonassen I, Goksøyr A (2021) Machine learning approaches for biomarker discovery using gene expression data. Exon Publications :53–64
Zhou R, Jiang F, Niu L, Song X, Yu L, Yang Y, Wu Z (2022) Increase crop resilience to heat stress using omic strategies. Front Plant Sci 13:891861
PubMed PubMed Central Google Scholar
Morita R, Hishinuma H, Ohyama H, Mega R, Ohta T, Nakagawa N, Agari Y, Fukui K, Shinkai A, Kuramitsu S, Masui R (2011) An alkyltransferase-like protein from Thermus thermophilus HB8 affects the regulation of gene expression in alkylation response. J Biochem 150(3):327-39. https://doi.org/10.1093/jb/mvr052
Xie Z, Feng Y, He Y, Lin Y, Wang X (2025) Identification of biomarkers for endometriosis based on summary-data-based Mendelian randomization and machine learning. Medicine (Baltimore), 104(14): e41804. https://doi.org/10.1097/MD.0000000000041804
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