{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T07:52:24Z","timestamp":1792050744888,"version":"4.4.0"},"reference-count":48,"publisher":"Wiley","issue":"8-9","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Environ and Mol Mutagen"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The application of machine learning approaches has shown promising results in cancer prediction. In the present study, data for nine gene\u2010based features that include\n                    <jats:italic>MLH1<\/jats:italic>\n                    (rs1800734),\n                    <jats:italic>MSH3<\/jats:italic>\n                    (rs26279),\n                    <jats:italic>MSH6<\/jats:italic>\n                    (rs3136228), and several variants within\n                    <jats:italic>MSH2<\/jats:italic>\n                    (rs2303423, rs4987188, rs17217772, rs63749993, rs2303426, and rs2303428), along with four additional features (Age, Gender, Smoking, and Pack Years) were used as input to develop a predictive model for assessing individual susceptibility to lung cancer. NumPy, Pandas, Scikit\u2010learn, and Matplotlib were major libraries used during the modeling process. The models included CatBoost, XGBoost, gradient boosting, random forest, support vector machine (SVM), K\u2010nearest neighbors (KNN), Gaussian Naive Bayes, and logistic regression, with their performance evaluated using stratified 5\u2010fold cross\u2010validation. The highest accuracy in predicting cancer predisposition was achieved by CatBoost (0.831\u2009\u00b1\u20090.014), with Pack Years (27.94%), Age (10.75%), and MLH1 (9.97%) as the major predictors. Precision, recall, and\n                    <jats:italic>F<\/jats:italic>\n                    1\u2010score were also used to assess performance, and all were within the range of 0.83\u20130.84. The models were further tested for performance across histological subtypes, gender, and smoking status. Gradient Boosting (0.868\u2009\u00b1\u20090.028) and Random Forest (0.902\u2009\u00b1\u20090.020) yielded the highest accuracy as histological subtype classifiers in ADCC and SCLC, respectively. When modeled using sex\u2010stratified and smoking\u2010status\u2010specific models, the ensemble methods significantly outperformed logistic regression and Gaussian Naive Bayes, with MMR gene variants (\n                    <jats:italic>MLH1<\/jats:italic>\n                    ,\n                    <jats:italic>MSH2<\/jats:italic>\n                    ) emerging as key modulators of histology and risk stratification. This highlights the potential of ensemble methods to bridge the gap between genetic epidemiology and molecular pathology, enabling more accurate risk assessment.\n                  <\/jats:p>","DOI":"10.1002\/em.70083","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T07:14:17Z","timestamp":1792048457000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Machine Learning Ensemble for Predicting Genetic Predisposition to Lung Cancer Based on Polymorphisms in the Mismatch\n                    <scp>DNA<\/scp>\n                    Repair Genes"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3673-6821","authenticated-orcid":false,"given":"Abhay","family":"Bagla","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Thapar Institute of Engineering &amp; Technology  Patiala India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-7662","authenticated-orcid":false,"given":"Sidhartha","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Biosciences and Bioengineering DY Patil International University  Pune India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heena","family":"Kansal","sequence":"additional","affiliation":[{"name":"Department of Biotechnology Thapar Institute of Engineering &amp; Technology  Patiala India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anup Kumar","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Indian Institute of Technology Indore  Indore India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9414-3771","authenticated-orcid":false,"given":"Siddharth","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Biotechnology Thapar Institute of Engineering &amp; Technology  Patiala India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3586-9315","authenticated-orcid":false,"given":"Puneet","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Indian Institute of Technology Indore  Indore India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0208-3922","authenticated-orcid":false,"given":"Vivek","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering Thapar Institute of Engineering &amp; Technology  Patiala India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.archoralbio.2016.09.006","article-title":"Repair Genes Expression Profile of MLH1, MSH2 and ATM in the Normal Oral Mucosa of Chronic Smokers","volume":"73","author":"Alves M. 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