{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:14:39Z","timestamp":1776183279943,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T00:00:00Z","timestamp":1585008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program."],"award-info":[{"award-number":["This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program."]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up\/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes\u2019 expressions using sparse autoencoders. Subsequently, the generated genes\u2019 expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.<\/jats:p>","DOI":"10.3390\/a13030073","type":"journal-article","created":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T13:04:04Z","timestamp":1585055044000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV"],"prefix":"10.3390","volume":"13","author":[{"given":"Nagwan M.","family":"Abdel Samee","sequence":"first","affiliation":[{"name":"Information Technology Department, College of Computer &amp; Information Sciences, Princess Nourah bint Abdulrahman University, 11671 Riyadh, Saudi Arabia"},{"name":"Computer Engineering Department, Misr University for Science and Technology, 12511 Giza, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4505","DOI":"10.18632\/oncotarget.2934","article-title":"Circulation long non-coding RNAs act as biomarkers for predicting tumorigenesis and metastasis in hepatocellular carcinoma","volume":"6","author":"Tang","year":"2015","journal-title":"Oncotarget"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.jhep.2010.04.015","article-title":"Hepatitis C virus replication cycle","volume":"53","author":"Bartenschlager","year":"2010","journal-title":"J. Hepatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1002\/hep.27160","article-title":"Index-based dietary patterns and risk of incident hepatocellular carcinoma and mortality from chronic liver disease in a prospective study","volume":"60","author":"Li","year":"2014","journal-title":"Hepatology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5404","DOI":"10.1128\/JVI.02529-09","article-title":"Gene expression profiling indicates the roles of host oxidative stress, apoptosis, lipid metabolism, and intracellular transport genes in the replication of hepatitis C virus","volume":"84","author":"Blackham","year":"2010","journal-title":"J. Virol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1126\/science.270.5235.467","article-title":"Quantitative monitoring of gene expression patterns with a complementary DNA microarray","volume":"270","author":"Schena","year":"1995","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, X., and Simon, R. (2011). Microarray-based cancer prediction using single genes. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-391"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.5213\/inj.2014.18.2.50","article-title":"Big data analysis using modern statistical and machine learning methods in medicine","volume":"18","author":"Yoo","year":"2014","journal-title":"Int. Neurourol. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3354","DOI":"10.3892\/ol.2015.3761","article-title":"Gene set enrichment and topological analyses based on interaction networks in pediatric acute lymphoblastic leukemia","volume":"10","author":"Sui","year":"2015","journal-title":"Oncol. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Urda, D., Luque-Baena, R.M., Franco, L., Jerez, J.M., and Sanchez-Marono, N. (2017, January 14\u201319). Machine learning models to search relevant genetic signatures in clinical context. Proceedings of the International Joint Conference on Neural Networks, Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966049"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yao, F., Coquery, J., and L\u00ea Cao, K.A. (2012). Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-24"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bellinger, C., Japkowicz, N., and Drummond, C. (2016, January 9\u201311). Synthetic oversampling for advanced radioactive threat detection. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.58"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10994-017-5670-4","article-title":"Manifold-based synthetic oversampling with manifold conformance estimation","volume":"107","author":"Bellinger","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Z., Xie, W., and Liu, T. (2018). Efficient feature selection and classification for microarray data. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0202167"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yue, Y., Li, Y., Yi, K., and Wu, Z. (2018, January 10\u201312). Synthetic Data Approach for Classification and Regression. Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors, Milan, Italy.","DOI":"10.1109\/ASAP.2018.8445094"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lyu, B., and Haque, A. (2018). Deep Learning Based Tumor Type Classification Using Gene Expression Data. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Association for Computing Machinery.","DOI":"10.1145\/3233547.3233588"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4802","DOI":"10.1109\/TNNLS.2017.2771290","article-title":"A Systematic Study of Online Class Imbalance Learning with Concept Drift","volume":"29","author":"Wang","year":"2018","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xie, R., Wen, J., Quitadamo, A., Cheng, J., and Shi, X. (2017). A deep auto-encoder model for gene expression prediction. BMC Genom., 18.","DOI":"10.1186\/s12864-017-4226-0"},{"key":"ref_18","first-page":"3371","article-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent Hugo Larochelle Yoshua Bengio Pierre-Antoine Manzagol","volume":"11","author":"Ca","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.3748\/wjg.v16.i24.3049","article-title":"Is inconsistency of \u03b1-fetoprotein level a good prognosticator for hepatocellular carcinoma recurrence?","volume":"16","author":"Hsieh","year":"2010","journal-title":"World J. Gastroenterol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/1477-7819-10-79","article-title":"Persistent increase in alpha-fetoprotein level in a patient without underlying liver disease who underwent curative resection of hepatocellular carcinoma. A case report and review of the literature","volume":"10","author":"Mannino","year":"2012","journal-title":"World J. Surg. Oncol."},{"key":"ref_21","first-page":"1055","article-title":"A novel hybrid method of gene selection and its application on tumor classification","volume":"Volume 5227","author":"Huang","year":"2008","journal-title":"Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence"},{"key":"ref_22","unstructured":"(2019, October 10). Advances in Kernel Methods-Support Vector Learning. Available online: https:\/\/www.researchgate.net\/publication\/2346087_Advances_in_Kernel_Methods_-_Support_Vector_Learning."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1198\/016214508000000337","article-title":"The Bayesian Lasso","volume":"103","author":"Park","year":"2008","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","article-title":"The Group Lasso for Logistic Regression","volume":"70","author":"Meier","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/S1672-0229(10)60022-8","article-title":"Mining Gene Expression Profiles: An Integrated Implementation of Kernel Principal Component Analysis and Singular Value Decomposition","volume":"8","author":"Reverter","year":"2010","journal-title":"Genom. Proteom. Bioinforma."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","article-title":"Nonlinear Component Analysis as a Kernel Eigenvalue Problem","volume":"10","author":"Smola","year":"1998","journal-title":"Neural Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.jspi.2007.06.019","article-title":"Several biplot methods applied to gene expression data","volume":"138","author":"Park","year":"2008","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jiph.2018.09.009","article-title":"A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique","volume":"12","author":"Nilashi","year":"2019","journal-title":"J. Infect. Public Health"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s12094-015-1447-z","article-title":"Expression of serum miR-218 in hepatocellular carcinoma and its prognostic significance","volume":"18","author":"Yang","year":"2016","journal-title":"Clin. Transl. Oncol."},{"key":"ref_30","first-page":"24","article-title":"Detection of biomarkers for Hepatocellular Carcinoma using a hybrid univariate gene selection methods","volume":"9","author":"Solouma","year":"2012","journal-title":"Theor. Biol. Med. Model."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1093\/nar\/30.1.207","article-title":"Gene Expression Omnibus: NCBI gene expression and hybridization array data repository","volume":"30","author":"Edgar","year":"2002","journal-title":"Nucleic Acids Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4251","DOI":"10.1109\/TSP.2017.2708023","article-title":"Efficient L1-Norm Principal-Component Analysis via Bit Flipping","volume":"65","author":"Markopoulos","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_33","first-page":"1","article-title":"FactoMineR: An R package for multivariate analysis","volume":"25","author":"Josse","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_34","unstructured":"(2019, October 11). Factoextra: Extract and Visualize the Results of Multivariate Data Analyses Version 1.0.5 from CRAN. Available online: https:\/\/rdrr.io\/cran\/factoextra\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"D54","DOI":"10.1093\/nar\/gki031","article-title":"Entrez Gene: Gene-centered information at NCBI","volume":"33","author":"Maglott","year":"2005","journal-title":"Nucleic Acids Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Masaki, S., Masutani, H., Yoshihara, E., and Yodoi, J. (2012). Deficiency of thioredoxin binding protein-2 (TBP-2) enhances TGF-\u03b2 signaling and promotes epithelial to mesenchymal transition. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0039900"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.canlet.2016.11.039","article-title":"STC2 as a novel mediator for Mus81-dependent proliferation and survival in hepatocellular carcinoma","volume":"388","author":"Wu","year":"2017","journal-title":"Cancer Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"E789","DOI":"10.1152\/ajpendo.00015.2013","article-title":"Asparagine synthetase: Regulation by cell stress and involvement in tumor biology","volume":"304","author":"Balasubramanian","year":"2013","journal-title":"Am. J. Physiol. Endocrinol. Metab."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1038\/s41598-017-01207-3","article-title":"In silico evaluation of DNA Damage Inducible Transcript 4 gene (DDIT4) as prognostic biomarker in several malignancies","volume":"7","author":"Pinto","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1074\/mcp.M113.035600","article-title":"Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics","volume":"13","author":"Fagerberg","year":"2014","journal-title":"Mol. Cell. Proteom."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.biochi.2016.12.010","article-title":"Dichotomous effects of isomeric secondary amines containing an aromatic nitrile and nitro group on human aortic smooth muscle cells via inhibition of cystathionine-\u03b3-lyase","volume":"133","author":"Ji","year":"2017","journal-title":"Biochimie"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, H.-P., Chang, H.-L., Bamodu, O.A., Yadav, V.K., Huang, T.-Y., Wu, A.T.H., Yeh, C.-T., Tsai, S.-H., and Lee, W.-H. (2019). Collagen 1A1 (COL1A1) Is a Reliable Biomarker and Putative Therapeutic Target for Hepatocellular Carcinogenesis and Metastasis. Cancers, 11.","DOI":"10.3390\/cancers11060786"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/15384047.2015.1108484","article-title":"Kr\u00fcppel-like factor 2 promotes cell proliferation in hepatocellular carcinoma through up-regulation of c-myc","volume":"17","author":"Zou","year":"2016","journal-title":"Cancer Biol. Ther."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1038\/s41388-017-0070-6","article-title":"Metabolic reprogramming by PCK1 promotes TCA cataplerosis, oxidative stress and apoptosis in liver cancer cells and suppresses hepatocellular carcinoma","volume":"37","author":"Liu","year":"2018","journal-title":"Oncogene"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.str.2012.12.019","article-title":"Recognition of mono-ADP-ribosylated ARTD10 substrates by ARTD8 macrodomains","volume":"21","author":"Forst","year":"2013","journal-title":"Structure"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1002\/jso.22095","article-title":"Differential expression of sprouty genes in hepatocellular carcinoma","volume":"105","author":"Sirivatanauksorn","year":"2012","journal-title":"J. Surg. Oncol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.dld.2016.08.114","article-title":"Meta-analysis of CYP2E1 polymorphisms in liver carcinogenesis","volume":"49","author":"Fu","year":"2017","journal-title":"Dig. Liver Dis."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8044","DOI":"10.1128\/MCB.25.18.8044-8051.2005","article-title":"Genetic evidence that small maf proteins are essential for the activation of antioxidant response element-dependent genes","volume":"25","author":"Katsuoka","year":"2005","journal-title":"Mol. Cell. Biol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"535","DOI":"10.3390\/v6020535","article-title":"CD81 and hepatitis C virus (HCV) infection","volume":"6","author":"Levy","year":"2014","journal-title":"Viruses"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1002\/2211-5463.12310","article-title":"Deep sequencing and comprehensive expression analysis identifies several molecules potentially related to human poorly differentiated hepatocellular carcinoma","volume":"7","author":"Shao","year":"2017","journal-title":"FEBS Open Bio"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.3892\/ol.2016.4969","article-title":"BCAT1 promotes tumor cell migration and invasion in hepatocellular carcinoma","volume":"12","author":"Xu","year":"2016","journal-title":"Oncol. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.ccr.2012.02.007","article-title":"Promotion of Hepatocellular Carcinoma by the Intestinal Microbiota and TLR4","volume":"21","author":"Dapito","year":"2012","journal-title":"Cancer Cell"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1016\/j.cell.2017.05.035","article-title":"Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing","volume":"169","author":"Zheng","year":"2017","journal-title":"Cell"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2710","DOI":"10.1039\/c2mb25160d","article-title":"Proteomic study explores AGR2 as pro-metastatic protein in HCC","volume":"8","author":"Yu","year":"2012","journal-title":"Mol. Biosyst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.tranon.2017.11.001","article-title":"ping eEF1A1 Overexpression Enhances Tumor Progression and Indicates Poor Prognosis in Hepatocellular Carcinoma","volume":"11","author":"Chen","year":"2018","journal-title":"Transl. Oncol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1242\/dmm.007831","article-title":"A high level of liver-specific expression of oncogenic Kras V12 drives robust liver tumorigenesis in transgenic zebrafish","volume":"4","author":"Nguyen","year":"2011","journal-title":"DMM Dis. Model. Mech."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Fu, L.-Y., Jia, H.-L., Dong, Q.-Z., Wu, J.-C., Zhao, Y., Zhou, H.-J., Ren, N., Ye, Q.-H., and Qin, L.-X. (2009). Suitable reference genes for real-time PCR in human HBV-related hepatocellular carcinoma with different clinical prognoses. BMC Cancer, 9.","DOI":"10.1186\/1471-2407-9-49"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3727\/105221618X15185539348147","article-title":"Deciphering the spectrum of mitochondrial DNA mutations in hepatocellular carcinoma using high-Throughput sequencing","volume":"18","author":"Yu","year":"2018","journal-title":"Gene Expr."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1002\/hep.21211","article-title":"Keratin variants associate with progression of fibrosis during chronic hepatitis C infection","volume":"43","author":"Strnad","year":"2006","journal-title":"Hepatology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6674","DOI":"10.3892\/mmr.2017.7457","article-title":"Molecular mechanisms of pathogenesis in hepatocellular carcinoma revealed by RNA-sequencing","volume":"16","author":"Liu","year":"2017","journal-title":"Mol. Med. Rep."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/3\/73\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:11:13Z","timestamp":1760173873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/3\/73"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,24]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["a13030073"],"URL":"https:\/\/doi.org\/10.3390\/a13030073","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,24]]}}}