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- Research Direction: biology
- Sci Category: SCIE
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BMC BIOINFORMATICS
Journal ISSN: 1471-2105
JCR: Q2
Impact Factor: 2.511
Articles
A new alignment-free method: K-mer Subsequence Natural Vector (K-mer SNV) for classification of fungi
Lily He, Mochao Huang, Gulinisha Yiming, Yi Zhu, Ruowei Liu,Jinghan Chen and Stephen S. T. Yau
Published: Keywords:K-mer SNV,Fungi,Alignment-free,Classification
Rprot-Vec: a deep learning approach for fast protein structure similarity calculation
Yichuan Zhang and Wen Zhang
Published: Doi:https://doi.org/10.1007/s11019-025-10281-8
Keywords:Protein structure similarity,Deep learning,Homology detection,Rprot-Vec,TM-score prediction,Bi-GRU.CNN,Protein sequence encoding
PhyloScape: interactive and scalable visualization platform for phylogenetic trees
Jiajia Wang, Bo Zhang, Linglu Zheng, Yan Chen, Chunyang Zhang, Guojiao Lin, Zhen Meng,Xuezhi Wang and Yuanchun Zhou
Published: Keywords:Phylogenetic tree visualization,JavaScript library,Plug-in,Web application,Evolution
EDRMM: enhancing drug recommendation via multi-granularity and multi-attribute representation
Feiyan Liu, Wenhao Wang, Jiawei Zheng, Yibo Xie, Xiaoli Wang and Dongxiang Zhang
Published:
An evaluation methodology for machine learning-based tandem mass spectra similarity prediction
Michael Strobel, Alberto Gil-de-la-Fuente, Mohammad Reza Zare Shahneh, Yasin El Abiead, Roman Bushuiev, Anton Bushuiev, Tomáš Pluskal and Mingxun Wang
Published: 11 July 2025
MKDESIGNER and TASEQ: a set of tools for plant genotyping by targeted amplicon sequencing
Koki Chigira、Masanori Yamasaki 和 Taiichiro Ookawa
Published:
RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA–protein binding sites prediction
Jiangbo Zhang,Yunhui Peng, Feifei Cui, Zilong Zhang, Shankai Yan & and Qingchen Zhang
Published: 11 July 2025
Doi:https://doi.org/10.1007/s11019-025-10281-8
Introduction
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing, and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
Editorial Board
- Alison Cuff — Durham University, Teesside University, University of Reading
- Eytan Domany — Weizmann Institute of Science
- João C. Setubal — University of São Paulo
- Jean-Philippe Vert — École Polytechnique
- Curtis Huttenhower — Harvard University
- Igor Jurisica — University of Toronto
- Rolf Apweiler — ETH Zurich (Swiss Federal Institute of Technology)
- Joshua Swamidass — University of Washington
- Danielle Talbot — University of York, University of Reading
Aims & Scope
- Computational Algorithms and Software: Development of new computational methods and tools for biological data analysis.
- Statistical Methods: Innovative statistical approaches for interpreting complex biological datasets.
- Machine Learning and Artificial Intelligence: Application of machine learning and artificial intelligence techniques to biological problems.
- Systems Biology: Modeling and analysis of biological systems and networks.
- Data Integration and Analysis: Methods for integrating and analyzing heterogeneous biological data sources.
- Bioinformatics Applications: Practical applications of bioinformatics in genomics, proteomics, metabolomics, and related fields.
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