{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T01:49:48Z","timestamp":1789004988421,"version":"build-2803163510"},"reference-count":40,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T00:00:00Z","timestamp":1600300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The recent advancements in cancer genomics have put under the spotlight DNA methylation, a genetic modification that regulates the functioning of the genome and whose modifications have an important role in tumorigenesis and tumor-suppression. Because of the high dimensionality and the enormous amount of genomic data that are produced through the last advancements in Next Generation Sequencing, it is very challenging to effectively make use of DNA methylation data in diagnostics applications, e.g., in the identification of healthy vs diseased samples. Additionally, state-of-the-art techniques are not fast enough to rapidly produce reliable results or efficient in managing those massive amounts of data. For this reason, we propose HD-classifier, an in-memory cognitive-based hyperdimensional (HD) supervised machine learning algorithm for the classification of tumor vs non tumor samples through the analysis of their DNA Methylation data. The approach takes inspiration from how the human brain is able to remember and distinguish simple and complex concepts by adopting hypervectors and no single numerical values. Exactly as the brain works, this allows for encoding complex patterns, which makes the whole architecture robust to failures and mistakes also with noisy data. We design and develop an algorithm and a software tool that is able to perform supervised classification with the HD approach. We conduct experiments on three DNA methylation datasets of different types of cancer in order to prove the validity of our algorithm, i.e., Breast Invasive Carcinoma (BRCA), Kidney renal papillary cell carcinoma (KIRP), and Thyroid carcinoma (THCA). We obtain outstanding results in terms of accuracy and computational time with a low amount of computational resources. Furthermore, we validate our approach by comparing it (i) to BIGBIOCL, a software based on Random Forest for classifying big omics datasets in distributed computing environments, (ii) to Support Vector Machine (SVM), and (iii) to Decision Tree state-of-the-art classification methods. Finally, we freely release both the datasets and the software on GitHub.<\/jats:p>","DOI":"10.3390\/a13090233","type":"journal-article","created":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T08:29:43Z","timestamp":1600331383000},"page":"233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2920-5838","authenticated-orcid":false,"given":"Fabio","family":"Cumbo","sequence":"first","affiliation":[{"name":"Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Via Sommarive 9, 38123 Povo Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2778-3709","authenticated-orcid":false,"given":"Eleonora","family":"Cappelli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Roma Tre, Via della Vasca Navale 79\/81, 00146 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8045-2925","authenticated-orcid":false,"given":"Emanuel","family":"Weitschek","sequence":"additional","affiliation":[{"name":"Department of Engineering, Uninettuno University, Corso Vittorio Emanuele II 39, 00186 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/nmeth1156","article-title":"Next-generation sequencing transforms today\u2019s biology","volume":"5","author":"Schuster","year":"2007","journal-title":"Nat. Methods"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trsl.2015.11.003","article-title":"The impact of next-generation sequencing on the DNA methylation\u2013based translational cancer research","volume":"169","author":"Soto","year":"2016","journal-title":"Transl. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cell.2013.09.006","article-title":"The next-generation sequencing revolution and its impact on genomics","volume":"155","author":"Koboldt","year":"2013","journal-title":"Cell"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.cell.2017.01.030","article-title":"Next-Generation Sequencing of Circulating Tumor DNA for Early Cancer Detection","volume":"168","author":"Aravanis","year":"2017","journal-title":"Cell"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/321209a0","article-title":"CpG-rich islands and the function of DNA methylation","volume":"321","author":"Bird","year":"1985","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1101\/gad.947102","article-title":"DNA methylation patterns and epigenetic memory","volume":"16","author":"Bird","year":"2002","journal-title":"Genes Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10120-014-0340-8","article-title":"DNA methylation downregulated mir-10b acts as a tumor suppressor in gastric cancer","volume":"18","author":"Li","year":"2015","journal-title":"Gastric Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1038\/srep01689","article-title":"RNA sequencing of cancer reveals novel splicing alterations","volume":"3","author":"Eswaran","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TCBB.2016.2607717","article-title":"Identifying Stages of Kidney Renal Cell Carcinoma by Combining Gene Expression and DNA Methylation Data","volume":"14","author":"Deng","year":"2016","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13040-018-0184-6","article-title":"Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction","volume":"11","author":"Cappelli","year":"2018","journal-title":"BioData Min."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.imu.2018.05.003","article-title":"Computational analysis of next generation sequencing data and its applications in clinical oncology","volume":"11","author":"Wadapurkar","year":"2018","journal-title":"Inform. Med. Unlocked"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Weitschek, E., Cumbo, F., Cappelli, E., and Felici, G. (2016, January 5\u20138). Genomic data integration: A case study on next generation sequencing of cancer. Proceedings of the 2016 27th International Workshop on Database and Expert Systems Applications (DEXA), Porto, Portugal.","DOI":"10.1109\/DEXA.2016.025"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.gene.2004.02.043","article-title":"Cytosine methylation and CpG, TpG (CpA) and TpA frequencies","volume":"333","author":"Jabbari","year":"2004","journal-title":"Gene"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1182\/blood-2017-03-735654","article-title":"The NCI Genomic Data Commons as an engine for precision medicine","volume":"130","author":"Jensen","year":"2017","journal-title":"Blood"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat. Genet."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117693510600200030","DOI":"10.1177\/117693510600200030","article-title":"Applications of machine learning in cancer prediction and prognosis","volume":"2","author":"Cruz","year":"2006","journal-title":"Cancer Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Weitschek, E., Cumbo, F., Cappelli, E., Felici, G., and Bertolazzi, P. (2018, January 3\u20136). Classifying big DNA methylation data: A gene-oriented approach. Proceedings of the International Conference on Database and Expert Systems Applications, Regensburg, Germany.","DOI":"10.1007\/978-3-319-99133-7_11"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ygeno.2014.07.004","article-title":"Classification of selectively constrained dna elements using feature vectors and rule-based classifiers","volume":"104","author":"Polychronopoulos","year":"2014","journal-title":"Genomics"},{"key":"ref_19","unstructured":"Tan, P., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, Addison Wesley."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.bdr.2018.02.005","article-title":"Classification of large DNA methylation datasets for identifying cancer drivers","volume":"13","author":"Celli","year":"2018","journal-title":"Big Data Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1093\/bioinformatics\/btv635","article-title":"CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules","volume":"32","author":"Cestarelli","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/(SICI)1096-8652(199810)59:2<143::AID-AJH7>3.0.CO;2-Y","article-title":"Decision-tree approach to the immunophenotype-based prognosis of the B-cell chronic lymphocytic leukemia","volume":"59","author":"Gagro","year":"1998","journal-title":"Am. J. Hematol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"35773","DOI":"10.1038\/srep35773","article-title":"Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree","volume":"6","author":"Li","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rahimi, A., Kanerva, P., and Rabaey, J.M. (2016, January 8\u201310). A robust and energy-efficient classifier using brain-inspired hyperdimensional computing. Proceedings of the 2016 International Symposium on Low Power Electronics and Design, San Francisco, CA, USA.","DOI":"10.1145\/2934583.2934624"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12559-009-9009-8","article-title":"Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors","volume":"1","author":"Kanerva","year":"2009","journal-title":"Cogn. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MCAS.2020.2988388","article-title":"Classification Using Hyperdimensional Computing: A Review","volume":"20","author":"Ge","year":"2020","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Imani, M., Kong, D., Rahimi, A., and Rosing, T. (2017, January 8\u20139). Voicehd: Hyperdimensional computing for efficient speech recognition. Proceedings of the 2017 IEEE International Conference on Rebooting Computing (ICRC), Washington, DC, USA.","DOI":"10.1109\/ICRC.2017.8123650"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Imani, M., Huang, C., Kong, D., and Rosing, T. (2018, January 24\u201328). Hierarchical hyperdimensional computing for energy efficient classification. Proceedings of the 2018 55th ACM\/ESDA\/IEEE Design Automation Conference (DAC), San Francisco, CA, USA.","DOI":"10.1109\/DAC.2018.8465708"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gupta, S., Imani, M., and Rosing, T. (2018, January 5\u20138). Felix: Fast and energy-efficient logic in memory. Proceedings of the 2018 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, USA.","DOI":"10.1145\/3240765.3240811"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Imani, M., Kim, Y., Riazi, S., Messerly, J., Liu, P., Koushanfar, F., and Rosing, T. (2019, January 8\u201313). A framework for collaborative learning in secure high-dimensional space. Proceedings of the 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), Milan, Italy.","DOI":"10.1109\/CLOUD.2019.00076"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kim, Y., Imani, M., and Rosing, T.S. (2018, January 15\u201318). Efficient human activity recognition using hyperdimensional computing. Proceedings of the 8th International Conference on the Internet of Things, Santa Barbara, CA, USA.","DOI":"10.1145\/3277593.3277617"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/JETCAS.2019.2935464","article-title":"A programmable hyper-dimensional processor architecture for human-centric IoT","volume":"9","author":"Datta","year":"2019","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Burrello, A., Schindler, K., Benini, L., and Rahimi, A. (2018, January 17\u201319). One-shot learning for iEEG seizure detection using end-to-end binary operations: Local binary patterns with hyperdimensional computing. Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), Cleveland, OH, USA.","DOI":"10.1109\/BIOCAS.2018.8584751"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Imani, M., Nassar, T., Rahimi, A., and Rosing, T. (2018, January 4\u20137). Hdna: Energy-efficient dna sequencing using hyperdimensional computing. Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2018.8333421"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kim, Y., Imani, M., Moshiri, N., and Rosing, T. (2020, January 9\u201313). GenieHD: Efficient DNA pattern matching accelerator using hyperdimensional computing. Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France.","DOI":"10.23919\/DATE48585.2020.9116397"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, B., and Dewey, C.N. (2011). RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-323"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Du, P., Zhang, X., Huang, C.C., Jafari, N., Kibbe, W.A., Hou, L., and Lin, S.M. (2010). Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-587"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Salamat, S., Imani, M., Khaleghi, B., and Rosing, T. (2019, January 24\u201326). F5-hd: Fast flexible fpga-based framework for refreshing hyperdimensional computing. Proceedings of the 2019 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Seaside, CA, USA.","DOI":"10.1145\/3289602.3293913"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Imani, M., Messerly, J., Wu, F., Pi, W., and Rosing, T. (2019, January 25\u201329). A binary learning framework for hyperdimensional computing. Proceedings of the 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy.","DOI":"10.23919\/DATE.2019.8714821"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/9\/233\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:10:46Z","timestamp":1760177446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/9\/233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,17]]},"references-count":40,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["a13090233"],"URL":"https:\/\/doi.org\/10.3390\/a13090233","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,17]]}}}