{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:10:08Z","timestamp":1791897008526,"version":"4.3.4"},"reference-count":94,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Lung cancer remains a leading cause of cancer-related mortality worldwide, with early and accurate diagnosis posing a critical challenge for improving patient outcomes. Gene expression data provide crucial insights for lung cancer classification by revealing underlying biological mechanisms. However, the high dimensionality of such data presents challenges, including computational complexity and overfitting risks. This study proposes a hybrid feature extraction framework combining Principal Component Analysis (PCA) and Mutual Information (MI) to address these issues. PCA reduces dimensionality by capturing key variance patterns, while MI selects features highly relevant to the target class, ensuring an informative and concise feature set. Gene expression datasets from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) were integrated, focusing on common genes. The hybrid PCA-MI framework was applied to rank genes, and the selected features were used to train a Convolutional Neural Network (CNN) for lung cancer classification. The genes ranked by the hybrid model were further analysed using protein-protein interaction (PPI) networks to identify hub genes, enhancing biological interpretability. The proposed framework was benchmarked against ten other feature extraction methods, including Lasso, Random Forest, Autoencoder, and PCA alone. The CNN classifier achieved superior performance with the PCA-MI features, attaining 98% accuracy and 98% precision. Training and validation curves demonstrated stable learning behaviour, and confusion matrix analysis confirmed robust predictions. Hub gene identification through PPI analysis validated the biological significance of the ranked genes. This study presents a robust framework for lung cancer classification by leveraging the strengths of PCA and MI, integrating deep learning and PPI analysis to address high-dimensional data challenges, and setting a foundation for future research in multi-omics data integration and enhanced diagnostic strategies.<\/jats:p>","DOI":"10.1371\/journal.pone.0342160","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T18:31:01Z","timestamp":1770316261000},"page":"e0342160","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["A hybrid feature extraction framework combining PCA and mutual information for gene expression based lung cancer classification"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3189-4217","authenticated-orcid":true,"given":"Syed Naseer Ahmad","family":"Shah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaartik","family":"Issar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafat","family":"Parveen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"pone.0342160.ref001","author":"LA Torre","year":"2016","journal-title":"Lung cancer statistics"},{"issue":"1","key":"pone.0342160.ref002","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s13045-023-01436-2","article-title":"Emerging evidence and treatment paradigm of non-small cell lung cancer","volume":"16","author":"S-YM Liu","year":"2023","journal-title":"J Hematol Oncol"},{"issue":"7689","key":"pone.0342160.ref003","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1038\/nature25183","article-title":"The biology and management of non-small cell lung cancer","volume":"553","author":"RS Herbst","year":"2018","journal-title":"Nature"},{"issue":"5","key":"pone.0342160.ref004","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1097\/PCR.0b013e318270ab43","article-title":"Peripheral Squamous Cell Carcinoma of the Lung","volume":"17","author":"H Durra","year":"2012","journal-title":"Pathology Case Reviews"},{"issue":"12","key":"pone.0342160.ref005","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/nrc.2017.87","article-title":"Small-cell lung cancer: what we know, what we need to know and the path forward","volume":"17","author":"AF Gazdar","year":"2017","journal-title":"Nat Rev Cancer"},{"issue":"9","key":"pone.0342160.ref006","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1097\/JTO.0000000000000630","article-title":"The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification","volume":"10","author":"WD Travis","year":"2015","journal-title":"J Thorac Oncol"},{"key":"pone.0342160.ref007","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3389\/fonc.2017.00193","article-title":"Lung Cancer: Understanding Its Molecular Pathology and the 2015 WHO Classification","volume":"7","author":"K Inamura","year":"2017","journal-title":"Front Oncol"},{"issue":"8","key":"pone.0342160.ref008","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nm733","article-title":"Gene-expression profiles predict survival of patients with lung adenocarcinoma","volume":"8","author":"DG Beer","year":"2002","journal-title":"Nat Med"},{"key":"pone.0342160.ref009","first-page":"627","article-title":"Gene expression data classification using topology and machine learning models","volume":"22","author":"TK Dey","year":"2022","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pone.0342160.ref010","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13062-020-00290-3","article-title":"A novel gene selection method for gene expression data for the task of cancer type classification","volume":"16","author":"N\u00d6 \u00d6zcan \u015e\u0130m\u015eek","year":"2021","journal-title":"Biol Direct"},{"key":"pone.0342160.ref011","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/978-3-030-34094-0_5","article-title":"Feature selection in high-dimensional data","author":"A Rouhi","year":"2020","journal-title":"Optimization, learning, and control for interdependent complex networks"},{"issue":"6","key":"pone.0342160.ref012","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1016\/j.eswa.2014.11.038","article-title":"Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering","volume":"42","author":"KK Bharti","year":"2015","journal-title":"Expert Systems with Applications"},{"key":"pone.0342160.ref013","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neucom.2016.07.080","article-title":"A hybrid feature selection algorithm for gene expression data classification","volume":"256","author":"H Lu","year":"2017","journal-title":"Neurocomputing"},{"key":"pone.0342160.ref014","doi-asserted-by":"crossref","first-page":"71427","DOI":"10.1109\/ACCESS.2022.3185226","article-title":"A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data","volume":"10","author":"H Almazrua","year":"2022","journal-title":"IEEE Access"},{"issue":"2","key":"pone.0342160.ref015","doi-asserted-by":"crossref","first-page":"562","DOI":"10.3390\/pr11020562","article-title":"Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data","volume":"11","author":"W Ali","year":"2023","journal-title":"Processes"},{"key":"pone.0342160.ref016","doi-asserted-by":"crossref","first-page":"78533","DOI":"10.1109\/ACCESS.2019.2922987","article-title":"A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification","volume":"7","author":"N Almugren","year":"2019","journal-title":"IEEE Access"},{"issue":"6","key":"pone.0342160.ref017","first-page":"5845","article-title":"Feature selection from high dimensional data based on iterative qualitative mutual information","volume":"36","author":"A Nagpal","year":"2019","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"pone.0342160.ref018","doi-asserted-by":"crossref","first-page":"105051","DOI":"10.1016\/j.compbiomed.2021.105051","article-title":"Feature selection methods on gene expression microarray data for cancer classification: A systematic review","volume":"140","author":"E Alhenawi","year":"2022","journal-title":"Comput Biol Med"},{"issue":"1","key":"pone.0342160.ref019","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/s12859-022-04689-9","article-title":"Prediction of lung cancer using gene expression and deep learning with KL divergence gene selection","volume":"23","author":"S Liu","year":"2022","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"pone.0342160.ref020","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1093\/bib\/bbl002","article-title":"Normalization and quantification of differential expression in gene expression microarrays","volume":"7","author":"C Steinhoff","year":"2006","journal-title":"Brief Bioinform"},{"key":"pone.0342160.ref021","doi-asserted-by":"crossref","DOI":"10.1177\/1177932219899051","article-title":"Multi-omics Data Integration, Interpretation, and Its Application","volume":"14","author":"I Subramanian","year":"2020","journal-title":"Bioinform Biol Insights"},{"key":"pone.0342160.ref022","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary","volume":"61","author":"A Fern\u00e1ndez","year":"2018","journal-title":"Journal of Artificial Intelligence Research"},{"key":"pone.0342160.ref023","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"Jair"},{"issue":"4","key":"pone.0342160.ref024","article-title":"Gene expression-based classification of non-small cell lung carcinomas and survival prediction","volume":"5","author":"J Hou","year":"2010","journal-title":"PLoS One"},{"key":"pone.0342160.ref025","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1201\/9781003438816-10","article-title":"Feature selection\/dimensionality reduction","volume-title":"Machine learning for healthcare systems","author":"D Stephen","year":"2023"},{"issue":"3","key":"pone.0342160.ref026","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/nbt0308-303","article-title":"What is principal component analysis?","volume":"26","author":"M Ringn\u00e9r","year":"2008","journal-title":"Nat Biotechnol"},{"issue":"7","key":"pone.0342160.ref027","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/nmeth.4346","article-title":"Principal component analysis","volume":"14","author":"J Lever","year":"2017","journal-title":"Nat Methods"},{"issue":"2","key":"pone.0342160.ref028","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.3390\/biomedinformatics4020070","article-title":"Cancer Classification from Gene Expression Using Ensemble Learning with an Influential Feature Selection Technique","volume":"4","author":"N Tabassum","year":"2024","journal-title":"BioMedInformatics"},{"issue":"1","key":"pone.0342160.ref029","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","article-title":"A review of feature selection methods based on mutual information","volume":"24","author":"JR Vergara","year":"2013","journal-title":"Neural Comput & Applic"},{"issue":"9","key":"pone.0342160.ref030","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0269126","article-title":"A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma","volume":"17","author":"O Abdelwahab","year":"2022","journal-title":"PLoS One"},{"key":"pone.0342160.ref031","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/ICAACE61206.2024.10548586","article-title":"Advancing Precision in Lung Cancer Subtyping: Integration of Machine Learning Feature Selection with MLP.","author":"H Yu","year":"2024","journal-title":"2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE)"},{"key":"pone.0342160.ref032","article-title":"Hybrid Approach for Feature Extraction of Lung Cancer Detection.","author":"T Patel","year":"2018","journal-title":"2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)"},{"key":"pone.0342160.ref033","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s12920-020-0677-2","article-title":"Convolutional neural network models for cancer type prediction based on gene expression","volume":"13","author":"M Mostavi","year":"2020","journal-title":"BMC Med Genomics"},{"key":"pone.0342160.ref034","first-page":"2282","article-title":"A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis","volume":"45","author":"C Gunavathi","year":"2021","journal-title":"Materials Today: Proceedings"},{"key":"pone.0342160.ref035","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1016\/j.csbj.2023.01.043","article-title":"Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine","volume":"21","author":"VB Mathema","year":"2023","journal-title":"Comput Struct Biotechnol J"},{"key":"pone.0342160.ref036","doi-asserted-by":"crossref","first-page":"4715998","DOI":"10.1155\/2022\/4715998","article-title":"Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile","volume":"2022","author":"HZ Almarzouki","year":"2022","journal-title":"J Healthc Eng"},{"key":"pone.0342160.ref037","first-page":"1","article-title":"Improved Adam Optimizer for Deep Neural Networks.","author":"Z Zhang","year":"2018","journal-title":"2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS)"},{"issue":"4","key":"pone.0342160.ref038","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/TNNLS.2019.2919608","article-title":"Completely automated CNN architecture design based on blocks","volume":"31","author":"Y Sun","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"pone.0342160.ref039","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/B978-0-12-815739-8.00011-0","article-title":"Autoencoders","volume-title":"Machine learning","author":"WHL Pinaya","year":"2020"},{"key":"pone.0342160.ref040","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/978-3-031-24628-9_16","article-title":"Autoencoders","author":"D Bank","year":"2023","journal-title":"Machine learning for data science handbook: data mining and knowledge discovery handbook"},{"key":"pone.0342160.ref041","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1186\/s12864-017-4226-0","article-title":"A deep auto-encoder model for gene expression prediction","volume":"18","author":"R Xie","year":"2017","journal-title":"BMC Genomics"},{"key":"pone.0342160.ref042","first-page":"1","article-title":"Principal component analysis (PCA)","author":"T Kurita","year":"2019","journal-title":"Computer vision: a reference guide"},{"issue":"3","key":"pone.0342160.ref043","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","article-title":"Principal components analysis (PCA)","volume":"19","author":"A Ma\u0107kiewicz","year":"1993","journal-title":"Computers & Geosciences"},{"issue":"9","key":"pone.0342160.ref044","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1093\/bioinformatics\/17.9.763","article-title":"Principal component analysis for clustering gene expression data","volume":"17","author":"KY Yeung","year":"2001","journal-title":"Bioinformatics"},{"issue":"7","key":"pone.0342160.ref045","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1016\/j.patcog.2008.10.028","article-title":"Feature selection with dynamic mutual information","volume":"42","author":"H Liu","year":"2009","journal-title":"Pattern Recognition"},{"issue":"4","key":"pone.0342160.ref046","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1016\/j.jnca.2011.01.002","article-title":"Mutual information-based feature selection for intrusion detection systems","volume":"34","author":"F Amiri","year":"2011","journal-title":"Journal of Network and Computer Applications"},{"key":"pone.0342160.ref047","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.procs.2015.03.178","article-title":"Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection","volume":"47","author":"CDA Vanitha","year":"2015","journal-title":"Procedia Computer Science"},{"key":"pone.0342160.ref048","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/SMC.2018.00080","article-title":"Autoencoder and Its Various Variants.","author":"J Zhai","year":"2018","journal-title":"2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"},{"key":"pone.0342160.ref049","doi-asserted-by":"crossref","first-page":"107547","DOI":"10.1016\/j.asoc.2021.107547","article-title":"Mutual information-based recommender system using autoencoder","volume":"109","author":"Z Noshad","year":"2021","journal-title":"Applied Soft Computing"},{"key":"pone.0342160.ref050","article-title":"Feature selection using lasso.","author":"V Fonti","year":"2017","journal-title":"VU Amsterdam research paper in business analytics"},{"key":"pone.0342160.ref051","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/ICACA.2016.7887916","article-title":"LASSO: A feature selection technique in predictive modeling for machine learning.","author":"R Muthukrishnan","year":"2016","journal-title":"2016 IEEE International Conference on Advances in Computer Applications (ICACA)"},{"key":"pone.0342160.ref052","article-title":"Gradient LASSO for feature selection.","author":"Y Kim","year":"2004","journal-title":"Proceedings of the twenty-first international conference on Machine learning"},{"issue":"2","key":"pone.0342160.ref053","first-page":"147","article-title":"Classification and selection of biomarkers in genomic data using LASSO","volume":"2005","author":"D Ghosh","year":"2005","journal-title":"J Biomed Biotechnol"},{"issue":"1","key":"pone.0342160.ref054","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random Forest","volume":"47","author":"SJ Rigatti","year":"2017","journal-title":"J Insur Med"},{"issue":"1","key":"pone.0342160.ref055","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Machine Learning"},{"issue":"4","key":"pone.0342160.ref056","doi-asserted-by":"crossref","first-page":"339","DOI":"10.30699\/ijp.2017.27990","article-title":"Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest","volume":"12","author":"M Ram","year":"2017","journal-title":"Iran J Pathol"},{"key":"pone.0342160.ref057","unstructured":"Nandhini B, Josephine RM. Annova test using SPSS software to find out the morphological leaf traits of five different genera. 2023."},{"key":"pone.0342160.ref058","unstructured":"Vaidya M, Kulkarni P. A review on gene selection for cancer classification from microarray data."},{"key":"pone.0342160.ref059","doi-asserted-by":"crossref","DOI":"10.1109\/ICASSP.2007.366913","article-title":"Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models.","author":"JR Hershey","year":"2007","journal-title":"2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP \u201907"},{"issue":"4","key":"pone.0342160.ref060","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1109\/TIT.2018.2805844","article-title":"Estimation of KL Divergence: Optimal Minimax Rate","volume":"64","author":"Y Bu","year":"2018","journal-title":"IEEE Trans Inform Theory"},{"key":"pone.0342160.ref061","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1109\/ICSMC.2006.385011","article-title":"Adaptive Emotion Recognition in Speech by Feature Selection Based on KL-divergence.","author":"T Noda","year":"2006","journal-title":"2006 IEEE International Conference on Systems, Man and Cybernetics"},{"issue":"9","key":"pone.0342160.ref062","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1109\/78.317858","article-title":"Analysis of the variance threshold of Kay\u2019s weighted linear predictor frequency estimator","volume":"42","author":"V Clarkson","year":"1994","journal-title":"IEEE Trans Signal Process"},{"key":"pone.0342160.ref063","author":"M Cuturi","year":"2013","journal-title":"Mean reversion with a variance threshold"},{"key":"pone.0342160.ref064","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/ICTS52701.2021.9608852","article-title":"Variance Threshold as Early Screening to Boruta Feature Selection for Intrusion Detection System.","author":"M Al Fatih Abil Fida","year":"2021","journal-title":"2021 13th International Conference on Information & Communication Technology and System (ICTS)"},{"issue":"6","key":"pone.0342160.ref065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3136625","article-title":"Feature selection: A data perspective","volume":"50","author":"J Li","year":"2017","journal-title":"ACM Comput Surv"},{"key":"pone.0342160.ref066","article-title":"Feature selection model development on near-infrared spectroscopy data","author":"R Raafi\u2019udin","year":"2023","journal-title":"Journal of Spectroscopy"},{"issue":"27","key":"pone.0342160.ref067","doi-asserted-by":"crossref","first-page":"42617","DOI":"10.1007\/s11042-023-15143-0","article-title":"Mutual information-based filter hybrid feature selection method for medical datasets using feature clustering","volume":"82","author":"S Asghari","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"pone.0342160.ref068","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"D Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"pone.0342160.ref069","article-title":"cytoHubba: identifying hub objects and sub-networks from complex interactome","author":"C-H Chin","year":"2014","journal-title":"BMC Syst Biol"},{"issue":"2","key":"pone.0342160.ref070","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1007\/s11010-020-03959-5","article-title":"Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis","volume":"476","author":"Y Wang","year":"2021","journal-title":"Mol Cell Biochem"},{"key":"pone.0342160.ref071","article-title":"Lung cancer biomarker identification from differential expression analysis using RNA-seq data for designing multitargeted drugs.","author":"SNA Shah","year":"2024","journal-title":"Biology and Life Sciences Forum"},{"issue":"10","key":"pone.0342160.ref072","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0007431","article-title":"Can survival prediction be improved by merging gene expression data sets?","volume":"4","author":"H Yasrebi","year":"2009","journal-title":"PLoS One"},{"key":"pone.0342160.ref073","article-title":"Efficient multimedia time series data retrieval under uniform scaling and normalisation.","author":"W Euachongprasit","year":"2008","journal-title":"Advances in Information Retrieval: 30th European Conference on IR Research, ECIR 2008, Glasgow, UK, March 30-April 3, 2008. Proceedings"},{"key":"pone.0342160.ref074","article-title":"Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm.","author":"P Jeatrakul","year":"2010","journal-title":"Neural Information Processing. Models and Applications: 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part II"},{"issue":"10","key":"pone.0342160.ref075","first-page":"4101","article-title":"Mutual information based PCA algorithm with application in process monitoring","volume":"66","author":"T Chudong","year":"2015","journal-title":"CIESC Journal"},{"key":"pone.0342160.ref076","doi-asserted-by":"crossref","first-page":"066138","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","author":"A Kraskov","year":"2004","journal-title":"Phys Rev E Stat Nonlin Soft Matter Phys"},{"issue":"6","key":"pone.0342160.ref077","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0066714","article-title":"Principal component analysis based feature extraction approach to identify circulating microRNA biomarkers","volume":"8","author":"Y Taguchi","year":"2013","journal-title":"PLoS One"},{"key":"pone.0342160.ref078","article-title":"A review of evaluation metrics in machine learning algorithms.","author":"G Naidu","year":"2023","journal-title":"Computer Science On-line Conference"},{"key":"pone.0342160.ref079","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"I Guyon","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"pone.0342160.ref080","first-page":"13","article-title":"Microarray Gene Expression Data for Detection Alzheimer\u2019s Disease Using k-means and Deep Learning.","author":"HM AL-Bermany","year":"2021","journal-title":"2021 7th International Engineering Conference \u201cResearch & Innovation amid Global Pandemic\" (IEC)"},{"issue":"2","key":"pone.0342160.ref081","doi-asserted-by":"crossref","first-page":"01","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A Review on Evaluation Metrics for Data Classification Evaluations","volume":"5","author":"M Hossin","year":"2015","journal-title":"IJDKP"},{"key":"pone.0342160.ref082","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"I Guyon","year":"2002","journal-title":"Machine Learning"},{"issue":"1","key":"pone.0342160.ref083","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"G Chandrashekar","year":"2014","journal-title":"Computers & Electrical Engineering"},{"key":"pone.0342160.ref084","unstructured":"Jovi\u0107 A, Brki\u0107 K, Bogunovi\u0107 N. A review of feature selection methods with applications. In: 2015."},{"issue":"1","key":"pone.0342160.ref085","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3758\/BF03213026","article-title":"Theoretical analysis of an alphabetic confusion matrix","volume":"9","author":"JT Townsend","year":"1971","journal-title":"Perception & Psychophysics"},{"issue":"2","key":"pone.0342160.ref086","doi-asserted-by":"crossref","DOI":"10.14569\/IJACSA.2021.0120225","article-title":"Hybrid Feature Selection and Ensemble Learning Methods for Gene Selection and Cancer Classification","volume":"12","author":"SN Qasem","year":"2021","journal-title":"IJACSA"},{"issue":"1","key":"pone.0342160.ref087","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"N Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"pone.0342160.ref088","article-title":"Optimization for deep learning: theory and algorithms","author":"R Sun","year":"2019","journal-title":"arXiv preprint"},{"key":"pone.0342160.ref089","doi-asserted-by":"crossref","DOI":"10.1162\/neco.1997.9.8.1735","volume-title":"Long Short-term Memory","author":"S Hochreiter","year":"1997"},{"issue":"19","key":"pone.0342160.ref090","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Y Saeys","year":"2007","journal-title":"Bioinformatics"},{"issue":"2065","key":"pone.0342160.ref091","first-page":"20150202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"IT Jolliffe","year":"2016","journal-title":"Philos Trans A Math Phys Eng Sci"},{"key":"pone.0342160.ref092","article-title":"Identification of common key genes and pathways between Covid-19 and lung cancer by using protein-protein interaction network analysis","author":"KS Nan","year":"2021","journal-title":"bioRxiv"},{"issue":"11","key":"pone.0342160.ref093","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: a software environment for integrated models of biomolecular interaction networks","volume":"13","author":"P Shannon","year":"2003","journal-title":"Genome Res"},{"issue":"6","key":"pone.0342160.ref094","first-page":"5859","article-title":"Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis","volume":"18","author":"Y Shi","year":"2019","journal-title":"Oncol Lett"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0342160","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T18:31:15Z","timestamp":1770316275000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0342160"}},"subtitle":[],"editor":[{"given":"Suyan","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":94,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0342160","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]}}}