会议信息

RECOMB 2027: International Conference on Research in Computational Molecular Biology

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RECOMB
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截稿日期:
2026-10-28 还有 32 天
通知日期:
2026-12-22
会议日期:
2027-05-17
会议地点:
Toronto, Ontario, Canada
届数:
CCF: B   ICORE: B   QUALIS: A2   浏览: 58098   关注: 19   参加: 4

会伴指数 (CP-I)

87.4 / 100
全站第 106 名 / 共 5,687 个会议 · 前 2%

生物信息学与健康信息学 第 2 / 184

学术认可 (35%)
92
投稿选择性 (20%)
95
会议传承 (20%)
93
社区关注 (10%)
48
资料公开度 (15%)
85

用到的输入: 收录等级：CCF B, ICORE B, QUALIS A2 · 录用率：15.9%（有记录的 5 届的均值） · 有据可查的届次：31 · 在会伴关注它的研究者：19 人 · 过去 24 个月打开过本页的研究者：11 人

公开资料里还缺: 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-26

征稿

RECOMB 2027 (International Conference on Research in Computational Molecular Biology) is a CCF B / ICORE B / QUALIS A2 conference held in Toronto, Ontario, Canada on 2027-05-17. The paper submission deadline is 2026-10-28. Acceptance notifications are sent on 2026-12-22.

Overview The RECOMB conference series, founded in 1997, provides a scientific forum for communicating advances in computational biology and applications in molecular biology and medicine. The conference aims to bridge the computational, mathematical, statistical, and biological sciences, bringing together researchers, professionals, students and industrial practitioners from all over the world for interaction and exchange of new developments in all areas of bioinformatics and computational biology. The conference will feature keynote talks by leading scientists, presentations of peer-reviewed high-quality research papers, and poster sessions on latest research progress. RECOMB 2027, the 31st Annual International Conference on Research in Computational Molecular Biology, will be held in Toronto, Canada, on May 17-20, 2027, with satellite meetings on May 15-16. Scope Papers reporting on original research (both theoretical and applied) in all areas of computational molecular biology are suitable for submission. RECOMB focuses on work with a substantial computational contribution or methodological advance: a new algorithm, model, statistical method, or theoretical result. Papers whose primary contribution is the application of existing methods to new data, or the assembly of a resource or pipeline without methodological novelty, are better suited to other venues. Topics Topics of interest include, but are not limited to: Algorithms and theory for computational biology Biological sequence algorithms, genome assembly, and pangenomics Metagenomics and microbiome analysis Gene regulation, transcriptomics, epigenomics and perturbation biology Single-cell and spatial omics Computational systems and network biology Protein sequence, structure and function Generative models for biomolecules Computational drug discovery and binder design RNA structure and function Computational proteomics, proteogenomics and metabolomics Population genetics and statistical genomics Cancer genomics and tumor evolution Evolution, phylogenetics, and comparative genomics Computational immunology and antibody design Representation learning for biology Causal inference, uncertainty quantification and calibration for biological applications Data privacy and security in biomedical research Papers from adjacent areas, for example agentic workflows or other AI systems for biology, are welcome provided they adhere to the computational and algorithmic scope. Submissions that do not may be returned without full review.
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录用率

平均录用率: 19.1% 15 年间 (2006–2020).

年份提交数录用数录用率(%)
20202063718%
20191753721.1%
2018193168.3%
20171842212%
20161723520.3%
20151703621.2%
20141543522.7%
20131673219.2%
20122003115.5%
20111534328.1%
20101763620.5%
20091663722.3%
20081933417.6%
20071703721.8%
20062173918%

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