Academic Journal
Quartile not availableBMC Systems Biology
About BMC Systems Biology
About BMC Systems Biology
BMC Systems Biology is a peer-reviewed scholarly journal published by Springer Science+Business Media.
Key indicators: ISSN 17520509.
Understanding This Journal's Metrics
Journal metrics help researchers assess fit, but they must be interpreted in context:
Publishing in BMC Systems Biology
When considering this journal, verify its current indexing status, editorial board, and recent publications to ensure it meets your needs for visibility and credibility.
- Author guidelines: Follow formatting, reference style, and article-type requirements exactly — non-compliant manuscripts may be returned without review.
- Submission: Submit via the official journal website.
Is This Journal Right for Your Paper?
- Audience match: Will the journal's readers cite and build on your findings?
- Timeline: Higher-tier journals mean longer review and higher rejection risk. Balance prestige against your schedule.
- Indexing: Confirm indexing in Web of Science, Scopus, and PubMed (if biomedical) meets your institutional requirements.
- Open access needs: Check funder mandates and whether the journal offers compliant OA options.
Frequently Asked Questions
What is the ISSN of BMC Systems Biology?
17520509.
Who publishes BMC Systems Biology?
Springer Science+Business Media.
Where do I submit to BMC Systems Biology?
Via the official website.
Is BMC Systems Biology peer-reviewed?
Yes — it is a peer-reviewed scholarly journal indexed in major academic databases.
Journal Metrics
A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice
Guan-Sheng Liu, Richard Ballweg, Alan Ashbaugh, Yin Zhang et al.
2019-12 · DOI: 10.1186/s12918-019-0708-9Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms
Yangyang Hao, Quan-Yang Duh, Richard T. Kloos, Joshua Babiarz et al.
2019-04 · DOI: 10.1186/s12918-019-0693-zFCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs
Xiaoying Li, Yaping Lin, Changlong Gu, Jialiang Yang et al.
2019-04 · DOI: 10.1186/s12918-019-0696-9Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks
Wenting Liu, Jagath C. Rajapakse
2019-04 · DOI: 10.1186/s12918-019-0695-xGNE: a deep learning framework for gene network inference by aggregating biological information
Kishan KC, Rui Li, Feng Cui, Qi Yu et al.
2019-04 · DOI: 10.1186/s12918-019-0694-yPredicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO
Hansheng Xue, Jiajie Peng, Xuequn Shang
2019-04 · DOI: 10.1186/s12918-019-0697-8Ultrafast clustering of single-cell flow cytometry data using FlowGrid
Xiaoxin Ye, Joshua W. K. Ho
2019-04 · DOI: 10.1186/s12918-019-0690-2Anti-TNF- αtreatment-related pathways and biomarkers revealed by transcriptome analysis in Chinese psoriasis patients
Lunfei Liu, Wenting Liu, Yuxin Zheng, Jisu Chen et al.
2019-04 · DOI: 10.1186/s12918-019-0698-7Boolean network modeling of β-cell apoptosis and insulin resistance in type 2 diabetes mellitus
Pritha Dutta, Lichun Ma, Yusuf Ali, Peter M.A. Sloot et al.
2019-04 · DOI: 10.1186/s12918-019-0692-0Network-based characterization of drug-protein interaction signatures with a space-efficient approach
Yasuo Tabei, Masaaki Kotera, Ryusuke Sawada, Yoshihiro Yamanishi et al.
2019-04 · DOI: 10.1186/s12918-019-0691-1A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data
Shiquan Sun, Yabo Chen, Yang Liu, Xuequn Shang et al.
2019-04 · DOI: 10.1186/s12918-019-0699-6PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach
Jihwan Ha, Chihyun Park, Sanghyun Park
2019-12 · DOI: 10.1186/s12918-019-0700-4Adaptive information processing of network modules to dynamic and spatial stimuli
J. Krishnan, Ioannis Floros
2019-12 · DOI: 10.1186/s12918-019-0703-1Correction to: Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone
Guillermo de Anda-Jáuregui, Kai Guo, Brett A. McGregor, Eva L. Feldman et al.
2019-12 · DOI: 10.1186/s12918-019-0707-xHow to schedule VEGF and PD-1 inhibitors in combination cancer therapy?
Xiulan Lai, Avner Friedman
2019-12 · DOI: 10.1186/s12918-019-0706-ySystems biology research at BGRS-2018
Yuriy L. Orlov, Ralf Hofestädt, Ancha V. Baranova
2019-03 · DOI: 10.1186/s12918-019-0685-zUrine proteome changes associated with autonomic regulation of heart rate in cosmonauts
Lyudmila H. Pastushkova, Vasily B. Rusanov, Anna G. Goncharova, Alexander G. Brzhozovskiy et al.
2019-03 · DOI: 10.1186/s12918-019-0688-9Effects of unpaired 1 gene overexpression on the lifespan of Drosophila melanogaster
Alexey Moskalev, Ekaterina Proshkina, Alex Zhavoronkov, Mikhail Shaposhnikov et al.
2019-03 · DOI: 10.1186/s12918-019-0687-xTowards embedding Caco-2 model of gut interface in a microfluidic device to enable multi-organ models for systems biology
Dmitry Sakharov, Diana Maltseva, Evgeny Knyazev, Sergey Nikulin et al.
2019-03 · DOI: 10.1186/s12918-019-0686-yLSM-W2: laser scanning microscopy worker for wheat leaf surface morphology
Ulyana S. Zubairova, Pavel Yu. Verman, Polina A. Oshchepkova, Alina S. Elsukova et al.
2019-03 · DOI: 10.1186/s12918-019-0689-8Reviews
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Version History
October 11, 2026 at 1:47 am
October 9, 2026