Result: BioMark: biomarker analysis tool.
Title:
BioMark: biomarker analysis tool.
Authors:
Balikci MA; Istanbul Technical University, 34867, Istanbul, Maslak, Turkey.; TUBITAK Bilgem, Kocaeli, Turkey., Njume CM; Istanbul Technical University, 34867, Istanbul, Maslak, Turkey., Cakmak A; Istanbul Technical University, 34867, Istanbul, Maslak, Turkey. ali.cakmak@itu.edu.tr.
Source:
BMC bioinformatics [BMC Bioinformatics] 2026 Jan 20. Date of Electronic Publication: 2026 Jan 20.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
References:
Zheng H, Zhang G, Zhang L, Wang Q, Li H, Han Y, et al. Comprehensive review of web servers and bioinformatics tools for cancer prognosis analysis. Front Oncol. 2020;10:68.
Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37(suppl–2):W652–60.
Chong J, Xia J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics. 2018;34(24):4313–4.
Mendes A, Havelund JF, Lemvig J, Schwämmle V, Færgeman NJ. MetaboLink: a web application for streamlined processing and analysis of large-scale untargeted metabolomics data. Bioinformatics. 2024;40(7):btae459.
Cardoso S, Afonso T, Maraschin M, Rocha M. WebSpecmine: a website for metabolomics data analysis and mining. Metabolites. 2019;9(10):237.
Madrid-Márquez L, Rubio-Escudero C, Pontes B, González-Pérez A, Riquelme JC, Sáez ME. MOMIC: a multi-omics pipeline for data analysis, integration and interpretation. Appl Sci. 2022;12(8):3987.
Gruca A, Henzel J, Kostorz I, Stȩclik T, Wróbel Ł, Sikora M. MAINE: a web tool for multi-omics feature selection and rule-based data exploration. Bioinformatics. 2022;38(6):1773–5.
Lu Y, Shen M, Yue L, Li C, Chen L, Wang X, Herrington D, Wang Y, Zhao Y, Fu T, Van Rechem C. “GenoCraft: A comprehensive, user-friendly web-based platform for high-throughput omics data analysis and visualization,” arXiv, 2024.
Zoppi J, Guillaume J-F, Neunlist M, Chaffron S. MiBiOmics: an interactive web application for multi-omics data exploration and integration. BMC Bioinformatics. 2021;22(1):6.
Camele G, Menazzi S, Chanfreau H, Marraco A, Hasperu W, Butti MD, et al. Multiomix: a cloud-based platform to infer cancer genomic and epigenomic events associated with gene expression modulation. Bioinformatics. 2022;38(3):866–8.
Lawrence M, Lee E-K, Cook D, Hofmann H, Wurtele E. “exploRase: Exploratory data analysis of systems biology data,” in Proceedings of the Fourth International Conference on Coordinated & Multiple Views in Exploratory Visualization (CMV’06). IEEE, 2006;40–79.
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84(11):5035–9.
Akhmedov M, Martinelli A, Geiger R, Kwee I. Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data. NAR Genomics and Bioinformatics. 2019;2(1):lqz019.
Zhou G, Ewald JD, Xia J. Omicsanalyst: a comprehensive web? based platform for visual analytics of multi?omics data. Nucleic Acids Res. 2021;49(W1):W476–82.
Netanely D, Stern N, Laufer I, Shamir R. PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets. BMC Bioinform. 2019;20(1):732.
Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 2016;44(D1):D463–70.
Zhou D, Zhu W, Sun T, Wang Y, Chi Y, Chen T, et al. iMAP: A web server for metabolomics data integrative analysis. Front Chem. 2021;9:659656.
Xie L, Wang Q, Nan F, Ge L, Dang Y, Sun X, et al. OSacc: gene expression-based survival analysis web tool for adrenocortical carcinoma. Cancer Manage Res. 2019;11:9145–52.
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.
Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498–503.
Wang F, Wang Q, Li N, Ge L, Yang M, An Y, et al. OSuvm: an interactive online consensus survival tool for uveal melanoma prognosis analysis. Mol Carcinog. 2020;59(1):56–61.
Zhang G, Wang Q, Yang M, Yuan Q, Dang Y, Sun X, et al. OSblca: a web server for investigating prognostic biomarkers of bladder cancer patients. Front Oncol. 2019;9:466.
Zhang G, Wang Q, Yang M, Yao X, Qi X, An Y, et al. OSpaad: an online tool to perform survival analysis by integrating gene expression profiling and long-term follow-up data of 1319 pancreatic carcinoma patients. Mol Carcinog. 2020;59(3):304–10.
An Y, Wang Q, Zhang L, Sun F, Zhang G, Dong H, et al. OSlgg: an online prognostic biomarker analysis tool for low-grade glioma. Front Oncol. 2020;10:1097.
An Y, Wang Q, Zhang G, Sun F, Zhang L, Li H, et al. OSlihc: an online prognostic biomarker analysis tool for hepatocellular carcinoma. Front Pharmacol. 2020;11:875.
Li J, Sang Y, Zeng S, Mo S, Zhang Z, He S, et al. MicrobioSee: a web-based visualization toolkit for multi-omics of microbiology. Front Genet. 2022;13:853612.
Camp RL, Dolled-Filhart M, Rimm DL. X-Tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252–9.
Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, et al. Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform. 2009;11(1):40–79.
Lundberg SM, Lee S-I. “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, 30. Curran Associates, Inc., 2017.
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67.
Ribeiro MT, Singh S, Guestrin C. ““why should i trust you?”: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016;1135–1144.
G. V. Cormack, C. L. A. Clarke, and S. Buettcher, “Reciprocal rank fusion outperforms condorcet and individual rank learning methods,” in Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2009, 758–759. [Online]. Available: https://doi.org/10.1145/1571941.1572114.
E. Benedetti, M. d. C. P. de Lima, A. C. de A. Garcia, A. Fidelis, T. F. de Almeida, B. P. F. de Souza, C. A. O. de Melo, D. C. de O. e Silva, F. R. S. de Souza, I. T. dos Santos, L. L. da C. e Silva, L. R. R. de Oliveira, M. C. T. de Oliveira, T. G. de Araújo, V. E. de C. da Silva, and A. G. de S. e Silva, “A multimodal atlas of tumor metabolism reveals the architecture of gene-metabolite covariation,” Oct. 2022. [Online]. Available: https://doi.org/10.5281/zenodo.7874228.
National Center for Biotechnology Information, “Serum metabolomics of TRAMP mice reveals novel biomarkers of prostate cancer - GSE120584,” 2019, accessed: August 9, 2025. [Online]. Available: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120584.
Jia J, Li F, Tang X-S, Xu S, Gao Y, Shi Q, et al. Long noncoding RNA DANCR promotes invasion of prostate cancer through epigenetically silencing expression of TIMP2/3. Oncotarget. 2016;7(25):17 449-17 461.
Lang C, Dai Y, Wu Z, Yang Q, He S, Zhang X, et al. SMAD3/SP1 complex-mediated constitutive active loop between lncRNA PCAT7 and TGF-β signaling promotes prostate cancer bone metastasis. Mol Oncol. 2020;14(4):808–28.
Huang Y, Zhu H, Liang Z, Wei W, Yang H, Wang Q, et al. Development and validation of a kinase-related gene signature as a novel diagnostic and prognostic model for prostate cancer. Biochimica et Biophysica Acta - Molecular Basis of Disease. 2025;1871(4):167722.
He Hong-jiang, Xue-feng Gu, Wan-hai Xu, Yang De-jun, Wang Xiao-min, Su Y. Krüppel-like factor 8 is a novel androgen receptor co-activator in human prostate cancer. Acta Pharmacologica Sinol. 2013;34(2):282–8.
Bashir SA, Alshalalfa M, Hegazy SA, Dolph M, Donnelly B, Bismar TA. Cysteine-rich secretory protein 3 (CRISP3), ERG and PTEN define a molecular subtype of prostate cancer with implication to patients’ prognosis. J Hematol Oncol. 2014;7(21):21.
Teoh SSY, Whisstock JC, Bird PI. Maspin (SERPINB5) is an obligate intracellular serpin. J Biol Chem. 2010;285(14):10 862-10 869.
Ye D, Wang Y, Deng X, Zhou X, Liu D, Zhou B, et al. DNMT3a-dermatopontin axis suppresses breast cancer malignancy via inactivating YAP. Cell Death Disease. 2023;14(2):106.
Xie W, Jiang Q, Wu X, Wang L. IKBKE phosphorylates and stabilizes Snail to promote breast cancer invasion and metastasis. Cell Death Differ. 2022;29(8):1528–40.
Sun Y, Ding Y, Guo C, Liu C, Ma P, Ma S, et al. α-parvin promotes breast cancer progression and metastasis through interaction with G3BP2 and regulation of TWIST1 signaling. Oncogene. 2019;38(24):4856–74.
Kim H-J, Seo B-G, Seo E-C, Lee K-M, Hwangbo C. Checkpoint Kinase 1 (CHK1) functions as both a diagnostic marker and a regulator of Epithelial-to-Mesenchymal Transition (EMT) in Triple-Negative Breast Cancer. Curr Issues Mol Biol. 2022;44(12):5848–65.
Pellecchia S, Sepe R, Federico A, Cuomo M, Credendino SC, Pisapia P, et al. The Metallophosphoesterase-Domain-Containing Protein 2 (MPPED2) gene acts as tumor suppressor in breast cancer. Cancers. 2019;11(6):797.
Mao Y, Fu A, Hoffman AE, Jacobs DI, Jin M, Chen K, et al. The circadian gene CRY2 is associated with breast cancer aggressiveness possibly via epigenomic modifications. Tumor Biol. 2015;36(5):3533–9.
Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37(suppl–2):W652–60.
Chong J, Xia J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics. 2018;34(24):4313–4.
Mendes A, Havelund JF, Lemvig J, Schwämmle V, Færgeman NJ. MetaboLink: a web application for streamlined processing and analysis of large-scale untargeted metabolomics data. Bioinformatics. 2024;40(7):btae459.
Cardoso S, Afonso T, Maraschin M, Rocha M. WebSpecmine: a website for metabolomics data analysis and mining. Metabolites. 2019;9(10):237.
Madrid-Márquez L, Rubio-Escudero C, Pontes B, González-Pérez A, Riquelme JC, Sáez ME. MOMIC: a multi-omics pipeline for data analysis, integration and interpretation. Appl Sci. 2022;12(8):3987.
Gruca A, Henzel J, Kostorz I, Stȩclik T, Wróbel Ł, Sikora M. MAINE: a web tool for multi-omics feature selection and rule-based data exploration. Bioinformatics. 2022;38(6):1773–5.
Lu Y, Shen M, Yue L, Li C, Chen L, Wang X, Herrington D, Wang Y, Zhao Y, Fu T, Van Rechem C. “GenoCraft: A comprehensive, user-friendly web-based platform for high-throughput omics data analysis and visualization,” arXiv, 2024.
Zoppi J, Guillaume J-F, Neunlist M, Chaffron S. MiBiOmics: an interactive web application for multi-omics data exploration and integration. BMC Bioinformatics. 2021;22(1):6.
Camele G, Menazzi S, Chanfreau H, Marraco A, Hasperu W, Butti MD, et al. Multiomix: a cloud-based platform to infer cancer genomic and epigenomic events associated with gene expression modulation. Bioinformatics. 2022;38(3):866–8.
Lawrence M, Lee E-K, Cook D, Hofmann H, Wurtele E. “exploRase: Exploratory data analysis of systems biology data,” in Proceedings of the Fourth International Conference on Coordinated & Multiple Views in Exploratory Visualization (CMV’06). IEEE, 2006;40–79.
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84(11):5035–9.
Akhmedov M, Martinelli A, Geiger R, Kwee I. Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data. NAR Genomics and Bioinformatics. 2019;2(1):lqz019.
Zhou G, Ewald JD, Xia J. Omicsanalyst: a comprehensive web? based platform for visual analytics of multi?omics data. Nucleic Acids Res. 2021;49(W1):W476–82.
Netanely D, Stern N, Laufer I, Shamir R. PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets. BMC Bioinform. 2019;20(1):732.
Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C, et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 2016;44(D1):D463–70.
Zhou D, Zhu W, Sun T, Wang Y, Chi Y, Chen T, et al. iMAP: A web server for metabolomics data integrative analysis. Front Chem. 2021;9:659656.
Xie L, Wang Q, Nan F, Ge L, Dang Y, Sun X, et al. OSacc: gene expression-based survival analysis web tool for adrenocortical carcinoma. Cancer Manage Res. 2019;11:9145–52.
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.
Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498–503.
Wang F, Wang Q, Li N, Ge L, Yang M, An Y, et al. OSuvm: an interactive online consensus survival tool for uveal melanoma prognosis analysis. Mol Carcinog. 2020;59(1):56–61.
Zhang G, Wang Q, Yang M, Yuan Q, Dang Y, Sun X, et al. OSblca: a web server for investigating prognostic biomarkers of bladder cancer patients. Front Oncol. 2019;9:466.
Zhang G, Wang Q, Yang M, Yao X, Qi X, An Y, et al. OSpaad: an online tool to perform survival analysis by integrating gene expression profiling and long-term follow-up data of 1319 pancreatic carcinoma patients. Mol Carcinog. 2020;59(3):304–10.
An Y, Wang Q, Zhang L, Sun F, Zhang G, Dong H, et al. OSlgg: an online prognostic biomarker analysis tool for low-grade glioma. Front Oncol. 2020;10:1097.
An Y, Wang Q, Zhang G, Sun F, Zhang L, Li H, et al. OSlihc: an online prognostic biomarker analysis tool for hepatocellular carcinoma. Front Pharmacol. 2020;11:875.
Li J, Sang Y, Zeng S, Mo S, Zhang Z, He S, et al. MicrobioSee: a web-based visualization toolkit for multi-omics of microbiology. Front Genet. 2022;13:853612.
Camp RL, Dolled-Filhart M, Rimm DL. X-Tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252–9.
Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, et al. Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform. 2009;11(1):40–79.
Lundberg SM, Lee S-I. “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, 30. Curran Associates, Inc., 2017.
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67.
Ribeiro MT, Singh S, Guestrin C. ““why should i trust you?”: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016;1135–1144.
G. V. Cormack, C. L. A. Clarke, and S. Buettcher, “Reciprocal rank fusion outperforms condorcet and individual rank learning methods,” in Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2009, 758–759. [Online]. Available: https://doi.org/10.1145/1571941.1572114.
E. Benedetti, M. d. C. P. de Lima, A. C. de A. Garcia, A. Fidelis, T. F. de Almeida, B. P. F. de Souza, C. A. O. de Melo, D. C. de O. e Silva, F. R. S. de Souza, I. T. dos Santos, L. L. da C. e Silva, L. R. R. de Oliveira, M. C. T. de Oliveira, T. G. de Araújo, V. E. de C. da Silva, and A. G. de S. e Silva, “A multimodal atlas of tumor metabolism reveals the architecture of gene-metabolite covariation,” Oct. 2022. [Online]. Available: https://doi.org/10.5281/zenodo.7874228.
National Center for Biotechnology Information, “Serum metabolomics of TRAMP mice reveals novel biomarkers of prostate cancer - GSE120584,” 2019, accessed: August 9, 2025. [Online]. Available: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120584.
Jia J, Li F, Tang X-S, Xu S, Gao Y, Shi Q, et al. Long noncoding RNA DANCR promotes invasion of prostate cancer through epigenetically silencing expression of TIMP2/3. Oncotarget. 2016;7(25):17 449-17 461.
Lang C, Dai Y, Wu Z, Yang Q, He S, Zhang X, et al. SMAD3/SP1 complex-mediated constitutive active loop between lncRNA PCAT7 and TGF-β signaling promotes prostate cancer bone metastasis. Mol Oncol. 2020;14(4):808–28.
Huang Y, Zhu H, Liang Z, Wei W, Yang H, Wang Q, et al. Development and validation of a kinase-related gene signature as a novel diagnostic and prognostic model for prostate cancer. Biochimica et Biophysica Acta - Molecular Basis of Disease. 2025;1871(4):167722.
He Hong-jiang, Xue-feng Gu, Wan-hai Xu, Yang De-jun, Wang Xiao-min, Su Y. Krüppel-like factor 8 is a novel androgen receptor co-activator in human prostate cancer. Acta Pharmacologica Sinol. 2013;34(2):282–8.
Bashir SA, Alshalalfa M, Hegazy SA, Dolph M, Donnelly B, Bismar TA. Cysteine-rich secretory protein 3 (CRISP3), ERG and PTEN define a molecular subtype of prostate cancer with implication to patients’ prognosis. J Hematol Oncol. 2014;7(21):21.
Teoh SSY, Whisstock JC, Bird PI. Maspin (SERPINB5) is an obligate intracellular serpin. J Biol Chem. 2010;285(14):10 862-10 869.
Ye D, Wang Y, Deng X, Zhou X, Liu D, Zhou B, et al. DNMT3a-dermatopontin axis suppresses breast cancer malignancy via inactivating YAP. Cell Death Disease. 2023;14(2):106.
Xie W, Jiang Q, Wu X, Wang L. IKBKE phosphorylates and stabilizes Snail to promote breast cancer invasion and metastasis. Cell Death Differ. 2022;29(8):1528–40.
Sun Y, Ding Y, Guo C, Liu C, Ma P, Ma S, et al. α-parvin promotes breast cancer progression and metastasis through interaction with G3BP2 and regulation of TWIST1 signaling. Oncogene. 2019;38(24):4856–74.
Kim H-J, Seo B-G, Seo E-C, Lee K-M, Hwangbo C. Checkpoint Kinase 1 (CHK1) functions as both a diagnostic marker and a regulator of Epithelial-to-Mesenchymal Transition (EMT) in Triple-Negative Breast Cancer. Curr Issues Mol Biol. 2022;44(12):5848–65.
Pellecchia S, Sepe R, Federico A, Cuomo M, Credendino SC, Pisapia P, et al. The Metallophosphoesterase-Domain-Containing Protein 2 (MPPED2) gene acts as tumor suppressor in breast cancer. Cancers. 2019;11(6):797.
Mao Y, Fu A, Hoffman AE, Jacobs DI, Jin M, Chen K, et al. The circadian gene CRY2 is associated with breast cancer aggressiveness possibly via epigenomic modifications. Tumor Biol. 2015;36(5):3533–9.
Grant Information:
124N069 Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; 124N069 Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; 1009742021 Ulusal Yüksek Başarımlı Hesaplama Merkezi, Istanbul Teknik Üniversitesi; TGA-2025-46998 Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi
Contributed Indexing:
Keywords: Artificial intelligence; Biomarker discovery; Disease diagnosis; Multivariate analysis
Entry Date(s):
Date Created: 20260120 Latest Revision: 20260120
Update Code:
20260121
DOI:
10.1186/s12859-025-06346-3
PMID:
41559544
Database:
MEDLINE
Further Information
Biomarkers play a pivotal role in disease diagnosis and prognosis by offering molecular insights into biological states. The rapid growth of high-throughput omics technologies has enabled the generation of large-scale biomarker datasets, yet analyzing these complex, high-dimensional data remains a major challenge-particularly for researchers lacking advanced computational expertise. While numerous tools exist for omics data analysis, many fall short in providing an integrated, user-friendly environment tailored specifically for biomarker discovery and interpretation. To address this gap, we present BioMark, a web-based platform designed to streamline biomarker analysis across diverse omics types. BioMark integrates robust statistical methods with widely used machine learning algorithms to support key workflows including statistical analysis, dimensionality reduction, classification, and subsequent model explanation. The platform emphasizes accessibility, offering intuitive visualizations and automated reporting to facilitate interpretation and dissemination of results. Notably, BioMark also offers a feature-ranking strategy that consolidates outputs from multiple analytical methods, enhancing the robustness of biomarker identification. By lowering the barrier to advanced biomarker analytics, BioMark empowers a broader range of researchers to uncover clinically relevant molecular signatures and accelerate translational research. Biomark is available online at https://bioinf.itu.edu.tr/biomark.
(© 2026. The Author(s).)
Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Conflict of interest: The authors declare no Conflict of interest. Availability and Requirements: Project name: BioMark Project home page: https://bioinf.itu.edu.tr/biomarkOperating system(s): Platform independent Programming language: Node.js (JavaScript) and Python Other requirements: Python 3.8 or higher License: GNU GPL Any restrictions to use by non-academics: License needed.