{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:14:04Z","timestamp":1791915244206,"version":"4.3.4"},"reference-count":69,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zefat Academic College"},{"name":"Abdullah Gul University Support Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The computational and interpretational difficulties caused by the ever-increasing dimensionality of biological data generated by new technologies pose a significant challenge. Feature selection (FS) methods aim to reduce the dimension, and feature grouping has emerged as a foundation for FS techniques that seek to detect strong correlations among features and identify irrelevant features. In this work, we propose the Recursive Cluster Elimination with Intra-Cluster Feature Elimination (RCE-IFE) method that utilizes feature grouping and iterates grouping and elimination steps in a supervised context. We assess dimensionality reduction and discriminatory capabilities of RCE-IFE on various high-dimensional datasets from different biological domains. For a set of gene expression, microRNA (miRNA) expression, and methylation datasets, the performance of RCE-IFE is comparatively evaluated with RCE-IFE-SVM (the SVM-adapted version of RCE-IFE) and SVM-RCE. On average, RCE-IFE attains an area under the curve (AUC) of 0.85 among tested expression datasets with the fewest features and the shortest running time, while RCE-IFE-SVM (the SVM-adapted version of RCE-IFE) and SVM-RCE achieve similar AUCs of 0.84 and 0.83, respectively. RCE-IFE and SVM-RCE yield AUCs of 0.79 and 0.68, respectively when averaged over seven different metagenomics datasets, with RCE-IFE significantly reducing feature subsets. Furthermore, RCE-IFE surpasses several state-of-the-art FS methods, such as Minimum Redundancy Maximum Relevance (MRMR), Fast Correlation-Based Filter (FCBF), Information Gain (IG), Conditional Mutual Information Maximization (CMIM), SelectKBest (SKB), and eXtreme Gradient Boosting (XGBoost), obtaining an average AUC of 0.76 on five gene expression datasets. Compared with a similar tool, Multi-stage, RCE-IFE gives a similar average accuracy rate of 89.27% using fewer features on four cancer-related datasets. The comparability of RCE-IFE is also verified with other biological domain knowledge-based Grouping-Scoring-Modeling (G-S-M) tools, including mirGediNET, 3Mint, and miRcorrNet. Additionally, the biological relevance of the selected features by RCE-IFE is evaluated. The proposed method also exhibits high consistency in terms of the selected features across multiple runs. Our experimental findings imply that RCE-IFE provides robust classifier performance and significantly reduces feature size while maintaining feature relevance and consistency.<\/jats:p>","DOI":"10.7717\/peerj-cs.2528","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T03:00:27Z","timestamp":1738897227000},"page":"e2528","source":"Crossref","is-referenced-by-count":6,"title":["RCE-IFE: recursive cluster elimination with intra-cluster feature elimination"],"prefix":"10.7717","volume":"11","author":[{"given":"Cihan","family":"Kuzudisli","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Hasan Kalyoncu University, Gaziantep, Turkey"},{"name":"Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2272-6270","authenticated-orcid":true,"given":"Burcu","family":"Bakir-Gungor","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bahjat","family":"Qaqish","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, Chapel Hill, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8780-6303","authenticated-orcid":true,"given":"Malik","family":"Yousef","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Zefat Academic College, Zefat, Israel"},{"name":"Galilee Digital Health Research Center, Zefat Academic College, Zefat, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"10.7717\/peerj-cs.2528\/ref-1","doi-asserted-by":"publisher","first-page":"1264941","DOI":"10.3389\/fmicb.2023.1264941","article-title":"microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach","volume":"14","author":"Bakir-Gungor","year":"2023","journal-title":"Frontiers in Microbiology"},{"issue":"D1","key":"10.7717\/peerj-cs.2528\/ref-2","doi-asserted-by":"publisher","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: archive for functional genomics data sets\u2014update","volume":"41","author":"Barrett","year":"2012","journal-title":"Nucleic Acids Research"},{"key":"10.7717\/peerj-cs.2528\/ref-3","doi-asserted-by":"publisher","first-page":"e65088","DOI":"10.7554\/eLife.65088","article-title":"Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3","volume":"10","author":"Beghini","year":"2021","journal-title":"eLife"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-4","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1145\/1656274.1656280","article-title":"KNIME\u2014the Konstanz information miner: version 2.0 and beyond","volume":"11","author":"Berthold","year":"2009","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"10.7717\/peerj-cs.2528\/ref-5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: a new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"issue":"10053","key":"10.7717\/peerj-cs.2528\/ref-6","doi-asserted-by":"publisher","first-page":"102099","DOI":"10.1016\/j.bspc.2020.102099","article-title":"Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework","volume":"62","author":"Chaitra","year":"2020","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.7717\/peerj-cs.2528\/ref-7","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"issue":"8","key":"10.7717\/peerj-cs.2528\/ref-8","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.3390\/genes15081036","article-title":"A comprehensive review of bioinformatics tools for genomic biomarker discovery driving precision oncology","volume":"15","author":"Clark","year":"2024","journal-title":"Genes"},{"issue":"6","key":"10.7717\/peerj-cs.2528\/ref-9","doi-asserted-by":"publisher","first-page":"834","DOI":"10.3390\/cells12060834","article-title":"Bioinformatic miRNA-mRNAs analysis revels to miR-934 as a potential regulator of the epithelial-mesenchymal transition in triple-negative breast cancer","volume":"12","author":"Contreras-Rodr\u00edguez","year":"2023","journal-title":"Cells"},{"issue":"2","key":"10.7717\/peerj-cs.2528\/ref-10","doi-asserted-by":"publisher","first-page":"219","DOI":"10.4048\/jbc.2019.22.e19","article-title":"miR-148b-3p, miR-190b, and miR-429 regulate cell progression and act as potential biomarkers for breast cancer","volume":"22","author":"Dai","year":"2019","journal-title":"Journal of Breast Cancer"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-11","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1504\/IJDMB.2013.050977","article-title":"A novel multi-stage feature selection method for microarray expression data analysis","volume":"7","author":"Du","year":"2013","journal-title":"International Journal of Data Mining and Bioinformatics"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-12","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1186\/s12885-020-06857-7","article-title":"MiR-18a and miR-18b are expressed in the stroma of oestrogen receptor alpha negative breast cancers","volume":"20","author":"Egeland","year":"2020","journal-title":"BMC Cancer"},{"key":"10.7717\/peerj-cs.2528\/ref-13","doi-asserted-by":"publisher","first-page":"1139082","DOI":"10.3389\/fgene.2023.1139082","article-title":"GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning","volume":"14","author":"Ersoz","year":"2023","journal-title":"Frontiers in Genetics"},{"key":"10.7717\/peerj-cs.2528\/ref-14","first-page":"1531","article-title":"Fast binary feature selection with conditional mutual information","volume":"5","author":"Fleuret","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"10.7717\/peerj-cs.2528\/ref-15","doi-asserted-by":"publisher","first-page":"11782234241234771","DOI":"10.1177\/11782234241234771","article-title":"MicroRNA-190b targets RFWD3 in estrogen receptor-positive breast cancer","volume":"18","author":"Frick","year":"2024","journal-title":"Breast Cancer: Basic and Clinical Research"},{"issue":"6","key":"10.7717\/peerj-cs.2528\/ref-16","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1038\/s41587-020-0546-8","article-title":"Visualizing and interpreting cancer genomics data via the Xena platform","volume":"38","author":"Goldman","year":"2020","journal-title":"Nature Biotechnology"},{"issue":"5439","key":"10.7717\/peerj-cs.2528\/ref-17","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: class discovery and class prediction by gene expression monitoring","volume":"286","author":"Golub","year":"1999","journal-title":"Science"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-18","doi-asserted-by":"publisher","first-page":"105052","DOI":"10.1016\/j.knosys.2019.105052","article-title":"Distributed multi-label feature selection using individual mutual information measures","volume":"188","author":"Gonzalez-Lopez","year":"2020","journal-title":"Knowledge-Based Systems"},{"issue":"1\/3","key":"10.7717\/peerj-cs.2528\/ref-19","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Machine Learning"},{"key":"10.7717\/peerj-cs.2528\/ref-20","first-page":"181","article-title":"Practical feature subset selection for machine learning","author":"Hall","year":"1998"},{"issue":"5","key":"10.7717\/peerj-cs.2528\/ref-21","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.3892\/ijo.2016.3405","article-title":"miR-135b, upregulated in breast cancer, promotes cell growth and disrupts the cell cycle by regulating LATS2","volume":"48","author":"Hua","year":"2016","journal-title":"International Journal of Oncology"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/3597051","article-title":"Feature selection and feature stability measurement method for high-dimensional small sample data based on big data technology","volume":"2021","author":"Huang","year":"2021","journal-title":"Computational Intelligence and Neuroscience"},{"key":"10.7717\/peerj-cs.2528\/ref-23","doi-asserted-by":"publisher","first-page":"1076554","DOI":"10.3389\/fgene.2022.1076554","article-title":"miRdisNET: discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning","volume":"13","author":"Jabeer","year":"2023","journal-title":"Frontiers in Genetics"},{"issue":"9","key":"10.7717\/peerj-cs.2528\/ref-24","doi-asserted-by":"publisher","first-page":"4479","DOI":"10.1002\/hbm.23676","article-title":"Dynamic brain connectivity is a better predictor of PTSD than static connectivity: dynamic brain connectivity","volume":"38","author":"Jin","year":"2017","journal-title":"Human Brain Mapping"},{"issue":"11","key":"10.7717\/peerj-cs.2528\/ref-25","doi-asserted-by":"publisher","first-page":"e48692","DOI":"10.1371\/journal.pone.0048692","article-title":"Validation of expression patterns for nine miRNAs in 204 lymph-node negative breast cancers","volume":"7","author":"Jonsdottir","year":"2012","journal-title":"PLOS ONE"},{"key":"10.7717\/peerj-cs.2528\/ref-26","doi-asserted-by":"publisher","first-page":"207","DOI":"10.36548\/jaicn","article-title":"Early prediction of autism spectrum disorder by computational approaches to fMRI analysis with early learning technique","volume":"2","author":"Karunakaran","year":"2020","journal-title":"Journal of Artificial Intelligence and Capsule Networks"},{"issue":"8","key":"10.7717\/peerj-cs.2528\/ref-27","doi-asserted-by":"publisher","first-page":"e15666","DOI":"10.7717\/peerj.15666","article-title":"Review of feature selection approaches based on grouping of features","volume":"11","author":"Kuzudisli","year":"2023","journal-title":"PeerJ"},{"key":"10.7717\/peerj-cs.2528\/ref-28","doi-asserted-by":"publisher","DOI":"10.1101\/2024.02.28.580487","article-title":"RCE-IFE: recursive cluster elimination with intra-cluster feature elimination","author":"Kuzudisli","year":"2024"},{"issue":"6","key":"10.7717\/peerj-cs.2528\/ref-29","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1007\/s11682-019-00191-8","article-title":"Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets","volume":"14","author":"Lanka","year":"2020","journal-title":"Brain Imaging and Behavior"},{"key":"10.7717\/peerj-cs.2528\/ref-30","doi-asserted-by":"publisher","first-page":"101581","DOI":"10.1016\/j.tice.2021.101581","article-title":"miR-934 promotes breast cancer metastasis by regulation of PTEN and epithelial-mesenchymal transition","volume":"71","author":"Lu","year":"2021","journal-title":"Tissue and Cell"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-31","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TCBB.2010.44","article-title":"Improving the computational efficiency of recursive cluster elimination for gene selection","volume":"8","author":"Luo","year":"2011","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"3","key":"10.7717\/peerj-cs.2528\/ref-32","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: the kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochemia Medica"},{"key":"10.7717\/peerj-cs.2528\/ref-33","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/nature08821","article-title":"A human gut microbial gene catalogue established by metagenomic sequencing","volume":"464","author":"Qin","year":"2010","journal-title":"Nature"},{"issue":"2","key":"10.7717\/peerj-cs.2528\/ref-34","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.schres.2018.01.006","article-title":"Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level","volume":"214","author":"Palaniyappan","year":"2019","journal-title":"Schizophrenia Research"},{"key":"10.7717\/peerj-cs.2528\/ref-35","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"issue":"8","key":"10.7717\/peerj-cs.2528\/ref-36","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"10.7717\/peerj-cs.2528\/ref-37","doi-asserted-by":"publisher","first-page":"bbaa151","DOI":"10.1093\/bib\/bbaa151","article-title":"Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches","volume":"22","author":"Perscheid","year":"2021","journal-title":"Briefings in Bioinformatics"},{"key":"10.7717\/peerj-cs.2528\/ref-38","doi-asserted-by":"publisher","first-page":"972421","DOI":"10.3389\/fnano.2022.972421","article-title":"SKCV: stratified K-fold cross-validation on ML classifiers for predicting cervical cancer","volume":"4","author":"Prusty","year":"2022","journal-title":"Frontiers in Nanotechnology"},{"key":"10.7717\/peerj-cs.2528\/ref-39","doi-asserted-by":"publisher","first-page":"927312","DOI":"10.3389\/fbinf.2022.927312","article-title":"A review of feature selection methods for machine learning-based disease risk prediction","volume":"2","author":"Pudjihartono","year":"2022","journal-title":"Frontiers in Bioinformatics"},{"key":"10.7717\/peerj-cs.2528\/ref-40","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nature11450","article-title":"A metagenome-wide association study of gut microbiota in type 2 diabetes","volume":"490","author":"Qin","year":"2012","journal-title":"Nature"},{"issue":"12","key":"10.7717\/peerj-cs.2528\/ref-41","doi-asserted-by":"publisher","first-page":"e22666","DOI":"10.1016\/j.heliyon.2023.e22666","article-title":"miRGediNET: a comprehensive examination of common genes in miRNA-Target interactions and disease associations: insights from a grouping-scoring-modeling approach","volume":"9","author":"Qumsiyeh","year":"2023","journal-title":"Heliyon"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-42","doi-asserted-by":"publisher","first-page":"19955","DOI":"10.1038\/s41598-022-24421-0","article-title":"GediNET for discovering gene associations across diseases using knowledge based machine learning approach","volume":"12","author":"Qumsiyeh","year":"2022","journal-title":"Scientific Reports"},{"issue":"6","key":"10.7717\/peerj-cs.2528\/ref-43","doi-asserted-by":"publisher","first-page":"2843","DOI":"10.1002\/hbm.23551","article-title":"Compromised hippocampus-striatum pathway as a potential imaging biomarker of mild-traumatic brain injury and posttraumatic stress disorder","volume":"38","author":"Rangaprakash","year":"2017","journal-title":"Human Brain Mapping"},{"issue":"2","key":"10.7717\/peerj-cs.2528\/ref-44","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1016\/j.csbj.2024.02.018","article-title":"Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: a review","volume":"23","author":"Rosati","year":"2024","journal-title":"Computational and Structural Biotechnology Journal"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-45","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/nm0102-68","article-title":"Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning","volume":"8","author":"Shipp","year":"2002","journal-title":"Nature Medicine"},{"issue":"2","key":"10.7717\/peerj-cs.2528\/ref-46","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/S1535-6108(02)00030-2","article-title":"Gene expression correlates of clinical prostate cancer behavior","volume":"1","author":"Singh","year":"2002","journal-title":"Cancer Cell"},{"issue":"8","key":"10.7717\/peerj-cs.2528\/ref-47","doi-asserted-by":"publisher","first-page":"5106","DOI":"10.3390\/app13085106","article-title":"AMP-GSM: prediction of antimicrobial peptides via a grouping-scoring\u2013modeling approach","volume":"13","author":"S\u00f6ylemez","year":"2023","journal-title":"Applied Sciences"},{"key":"10.7717\/peerj-cs.2528\/ref-48","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1109\/TITB.2008.920787","article-title":"Recursive fuzzy granulation for gene subsets extraction and cancer classification","volume":"12","author":"Tang","year":"2008","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"key":"10.7717\/peerj-cs.2528\/ref-49","doi-asserted-by":"publisher","first-page":"68","DOI":"10.5114\/wo.2014.47136","article-title":"Review the cancer genome atlas (TCGA): an immeasurable source of knowledge","volume":"1A","author":"Tomczak","year":"2015","journal-title":"Wsp\u00f3\u0142czesna Onkologia"},{"key":"10.7717\/peerj-cs.2528\/ref-50","doi-asserted-by":"publisher","first-page":"1093326","DOI":"10.3389\/fgene.2023.1093326","article-title":"Invention of 3Mint for feature grouping and scoring in multi-omics","volume":"14","author":"Unlu Yazici","year":"2023","journal-title":"Frontiers in Genetics"},{"issue":"6871","key":"10.7717\/peerj-cs.2528\/ref-51","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"van\u2019t Veer","year":"2002","journal-title":"Nature"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-52","doi-asserted-by":"publisher","first-page":"101588","DOI":"10.1016\/j.bbrep.2023.101588","article-title":"miRNAs as biomarkers of therapeutic response to HER2-targeted treatment in breast cancer: a systematic review","volume":"37","author":"Vo","year":"2024","journal-title":"Biochemistry and Biophysics Reports"},{"key":"10.7717\/peerj-cs.2528\/ref-53","doi-asserted-by":"publisher","first-page":"1243874","DOI":"10.3389\/fgene.2023.1243874","article-title":"TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information","volume":"14","author":"Voskergian","year":"2023","journal-title":"Frontiers in Genetics"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-54","doi-asserted-by":"publisher","first-page":"30086","DOI":"10.1038\/srep30086","article-title":"Random bits forest: a strong classifier\/regressor for big data","volume":"6","author":"Wang","year":"2016","journal-title":"Scientific Reports"},{"key":"10.7717\/peerj-cs.2528\/ref-55","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3892\/ol.2019.10334","article-title":"Downregulation of miR\u2010505 promotes cell proliferation, migration and invasion, and predicts poor prognosis in breast cancer","volume":"65","author":"Wang","year":"2019","journal-title":"Oncology Letters"},{"issue":"4","key":"10.7717\/peerj-cs.2528\/ref-56","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.jmgm.2008.08.004","article-title":"Data mining PubChem using a support vector machine with the Signature molecular descriptor: classification of factor XIa inhibitors","volume":"27","author":"Weis","year":"2008","journal-title":"Journal of Molecular Graphics and Modelling"},{"issue":"20","key":"10.7717\/peerj-cs.2528\/ref-57","doi-asserted-by":"publisher","first-page":"4020","DOI":"10.1093\/bioinformatics\/btz204","article-title":"maTE: discovering expressed interactions between microRNAs and their targets","volume":"35","author":"Yousef","year":"2019","journal-title":"Bioinformatics"},{"key":"10.7717\/peerj-cs.2528\/ref-58","doi-asserted-by":"publisher","DOI":"10.1101\/2024.03.30.585514","article-title":"G-S-M: a comprehensive framework for integrative feature selection in omics data analysis and beyond","author":"Yousef","year":"2024"},{"key":"10.7717\/peerj-cs.2528\/ref-59","doi-asserted-by":"publisher","first-page":"767455","DOI":"10.3389\/fgene.2022.767455","article-title":"miRModuleNet: detecting miRNA-mRNA regulatory modules","volume":"13","author":"Yousef","year":"2022","journal-title":"Frontiers in Genetics"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-60","doi-asserted-by":"publisher","first-page":"e11458","DOI":"10.7717\/peerj.11458","article-title":"miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking","volume":"9","author":"Yousef","year":"2021","journal-title":"PeerJ"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-61","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1186\/1471-2105-8-144","article-title":"Recursive cluster elimination (RCE) for classification and feature selection from gene expression data","volume":"8","author":"Yousef","year":"2007","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-62","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/e23010002","article-title":"Application of biological domain knowledge based feature selection on gene expression data","volume":"23","author":"Yousef","year":"2020","journal-title":"Entropy"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-63","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s12859-023-05187-2","article-title":"PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach","volume":"24","author":"Yousef","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"10.7717\/peerj-cs.2528\/ref-64","doi-asserted-by":"publisher","first-page":"893378","DOI":"10.3389\/fgene.2022.893378","article-title":"TextNetTopics: text classification based word grouping as topics and topics\u2019 scoring","volume":"13","author":"Yousef","year":"2022","journal-title":"Frontiers in Genetics"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-65","doi-asserted-by":"publisher","first-page":"e336","DOI":"10.7717\/peerj-cs.336","article-title":"CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis","volume":"7","author":"Yousef","year":"2021","journal-title":"PeerJ Computer Science"},{"key":"10.7717\/peerj-cs.2528\/ref-66","first-page":"856","article-title":"Feature selection for high-dimensional data: a fast correlation-based filter solution","author":"Yu","year":"2003"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-67","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s12943-018-0818-9","article-title":"miR-190 suppresses breast cancer metastasis by regulation of TGF-\u03b2-induced epithelial-mesenchymal transition","volume":"17","author":"Yu","year":"2018","journal-title":"Molecular Cancer"},{"key":"10.7717\/peerj-cs.2528\/ref-68","doi-asserted-by":"publisher","first-page":"766","DOI":"10.15252\/msb.20145645","article-title":"Potential of fecal microbiota for early-stage detection of colorectal cancer","volume":"10","author":"Zeller","year":"2014","journal-title":"Molecular Systems Biology"},{"issue":"1","key":"10.7717\/peerj-cs.2528\/ref-69","doi-asserted-by":"publisher","first-page":"5349","DOI":"10.1038\/s41598-017-05776-1","article-title":"Joint covariate detection on expression profiles for identifying microRNAs related to venous metastasis in hepatocellular carcinoma","volume":"7","author":"Zhao","year":"2017","journal-title":"Scientific Reports"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-2528.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2528.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2528.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2528.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T03:00:45Z","timestamp":1738897245000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-2528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,7]]},"references-count":69,"alternative-id":["10.7717\/peerj-cs.2528"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.2528","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.02.28.580487","asserted-by":"object"}]},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,7]]},"article-number":"e2528"}}