SCIEQ1
BioData Mining
BMC
BioData Mining is an academic journal published by BMC. Identifiers: ISSN 1756-0381, eISSN 1756-0381. Indexed in SCIE. Metrics: JIF 6.1. Subject areas: MATHEMATICAL & COMPUTATIONAL BIOLOGY. tlooto lists 841 papers from this journal.
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Journal profile
- ISSN
- 1756-0381
- eISSN
- 1756-0381
- Abbreviation
- BIODATA MIN
- Publisher
- BMC
- Country
- -
Web of Science categories
SCIEMATHEMATICAL & COMPUTATIONAL BIOLOGY
Scopus ASJC categories
No ASJC category data available.
Keywords
Mathematical & Computational Biology
Papers in this journal
Recent papers
- A crisis of overconfidence: Why confidence, not accuracy, is the real risk in clinical AI
2026 · 1 citations
- KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model
2026
- Early prediction of longitudinal treatment adherence in obstructive sleep apnea using machine learning approaches
2026
- Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models
2026
- Machine learning-based assessment of the healthy human gut mycobiota landscape using ITS1 DNA metabarcoding data
2026
Most cited papers
- Using graph theory to analyze biological networks
2011 · 787 citations
- The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
2021 · 779 citations
- Ten quick tips for machine learning in computational biology
2017 · 772 citations
- Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks
2015 · 528 citations
- The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
2023 · 512 citations