Open Access
Issue
BIO Web Conf.
Volume 232, 2026
2026 16th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2026)
Article Number 01007
Number of page(s) 12
Section Bioinformatics Algorithms and Advanced Omics Data Analysis
DOI https://doi.org/10.1051/bioconf/202623201007
Published online 24 April 2026
  • J. Kim, A. Harper, V. McCormack, H. Sung, N. Houssami, E. Morgan, M. Mutebi, G. Garvey, I. Soerjomataram, M.M. Fidler-Benaoudia, Global patterns and trends in breast cancer incidence and mortality across 185 countries, Nature Medicine 31, 1154 (2025). 10.1038/s41591-025-03502-3 [Google Scholar]
  • International Agency for Research on Cancer, Tech. rep., World Health Organization (2025), press Release No. 361 [Google Scholar]
  • The Cancer Genome Atlas Network, Comprehensive molecular portraits of human breast tumours, Nature 490, 61 (2012). 10.1038/nature11412 [Google Scholar]
  • T. Sørlie, C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications, Proceedings of the National Academy of Sciences 98, 10869 (2001). 10.1073/pnas.191367098 [Google Scholar]
  • Genomic Data Commons, GDC Data Portal Documentation (2024), https://portal.gdc.cancer.gov [Google Scholar]
  • M.I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for rna-seq data with deseq2, Genome Biology 15, 550 (2014). 10.1186/s13059-014-0550-8 [Google Scholar]
  • J.T. Leek, R.B. Scharpf, H.C. Bravo, D. Simcha, B. Langmead, W.E. Johnson, D. Geman, K. Baggerly, R.A. Irizarry, Tackling the widespread and critical impact of batch effects in high-throughput data, Nature Reviews Genetics 11, 733 (2010). 10.1038/nrg2825 [Google Scholar]
  • W.E. Johnson, C. Li, A. Rabinovic, Adjusting batch effects in microarray expression data using empirical bayes methods, Biostatistics 8, 118 (2007). 10.1093/biostatistics/kxj037 [Google Scholar]
  • J. Yang, M. Li, W. Chen, Mlomics: Machine learning framework for multi-omics data integration, Bioinformatics (2025), in press. [Google Scholar]
  • A. Colaprico, T.C. Silva, C. Olsen, L. Garofano, C. Cava, D. Garolini, T.S. Sabedot, T.M. Malta, S.M. Pagnotta, I. Castiglioni et al., Tcgabiolinks: an r/bioconductor package for integrative analysis of tcga data, Nucleic Acids Research 44, e71 (2016). 10.1093/nar/gkv1507 [Google Scholar]
  • M. Picard, M.P. Scott-Boyer, A. Bodein, O. Périn, A. Droit, Integration strategies of multi-omics data for machine learning analysis, Computational and Structural Biotechnology Journal 19, 3735 (2021). 10.1016/j.csbj.2021.06.030 [Google Scholar]
  • L. Chen, X. Pan, Y.H. Zhang, M. Liu, T. Huang, Y.D. Cai, Classification of widely and rarely expressed genes with recurrent neural network, Computational and Structural Biotechnology Journal 17, 49 (2019). 10.1016/j.csbj.2018.12.002 [Google Scholar]
  • A. Dobin, C.A. Davis, F. Schlesinger, J. Drenkow, C. Zaleski, S. Jha, P. Batut, M. Chaisson, T.R. Gingeras, Star: ultrafast universal rna-seq aligner, Bioinformatics 29, 15 (2013). 10.1093/bioinformatics/bts635 [Google Scholar]
  • M. Bibikova, B. Barnes, C. Tsan, V. Ho, B. Klotzle, J.M. Le, D. Delano, L. Zhang, G.P. Schroth, K.L. Gunderson et al., High density dna methylation array with single cpg site resolution, Genomics 98, 288 (2011). 10.1016/j.ygeno.2011.07.007 [Google Scholar]
  • J. Zhu, J.Z. Sanborn, S. Benz, C. Szeto, F. Hsu, R.M. Kuhn, D. Karolchik, J. Archie, M.E. Lenburg, L.J. Esserman et al., The tcga-assembler 2: software pipeline for retrieval and processing of tcga/cptac data, Bioinformatics 30, 1635 (2014). 10.1093/bioinfor-matics/btu085 [Google Scholar]
  • C.H. Mermel, S.E. Schumacher, B. Hill, M.L. Meyerson, R. Beroukhim, G. Getz, Gistic2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers, Genome Biology 12, R41 (2011). 10.1186/gb-2011-12-4-r41 [Google Scholar]
  • W. Zhao, E. Serpedin, in Encyclopedia of Systems Biology (Springer, 2021) [Google Scholar]
  • P. Du, X. Zhang, C.C. Huang, N. Jafari, W.A. Kibbe, L. Hou, S.M. Lin, Comparison of beta-value and m-value methods for quantifying methylation levels by microarray analysis, BMC Bioinformatics 11, 587 (2010). 10.1186/1471-2105-11-587 [Google Scholar]
  • K. Yoshihara, M. Shahmoradgoli, E. Martínez, R. Vegesna, H. Kim, W. Torres-Garcia, V. Treviño, H. Shen, P.W. Laird, D.A. Levine et al., Inferring tumour purity and stromal and immune cell admixture from expression data, Nature Communications 4, 2612 (2013). 10.1038/ncomms3612 [Google Scholar]
  • O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, R.B. Altman, Missing value estimation methods for dna microarrays, Bioinformatics 17, 520 (2001). 10.1093/bioinformatics/17.6.520 [Google Scholar]
  • R.D. Peng, Reproducible research in computational science, Science 334, 1226 (2011). 10.1126/science.1213847 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.