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NAR Genomics and Bioinformatics (NAR GB)

Publisher :

Oxford University Press

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Biology
  • Genomics
  • Hi-C

    • +12

e-ISSN :

2631-9268

Issue Frequency :

Monthly

Impact Factor :

4.0

Est. Year :

2019

Mobile :

441865556767

Country :

United Kingdom

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

cedric.notredame@crg.eu

Journal Descriptions

NAR Genomics and Bioinformatics is an interdisciplinary journal focused on genomics and bioinformatics large-scale data analysis. It aims at providing the community with high quality results, analysis and methods in all aspects of genomics and bioinformatics. Reproducibility is a strong focus of the journal, and all entries will have to comply with strict guidelines ensuring the perfect reproducibility of both experimental and bioinformatics analysis. Standard papers are expected to be compliant with NAR main guidelines and entries must fulfill the condition of scientific quality, novelty, timeliness, usefulness and usability, as established through an extensive peer-reviewing process. In the case of bioinformatics methods and analysis, usability implies a suitable implementation of the FAIR principle that requires data and software to be Findable, Accessible, Interoperable and Re-usable. NAR Genomics and Bioinformatics has a strict open-source policy and will only consider for publication contributions whose novel bioinformatics components are open source.


NAR Genomics and Bioinformatics (NAR GB) is :

International, Peer-Reviewed, Open Access, Refereed, Biology, Genomics, Hi-C, Metabolomics, Structural Biology, Omics Analysis, Functional genomics, Single Cell Analysis, Gene Regulation, Sequence Analysis, Human Health, Plant Biology, Microbiology, Statistical Learning, Mathematics , Online Monthly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 2631-9268, Established in - 2019, Impact Factor - 4.0

Not Provide Crossref DOI

Indexed in Scopus, WoS, DOAJ, PubMed

Not indexed in UGC CARE

Indexing

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Publications of NAR GB

Research Article
  • dott image Francesco Ceccarelli
  • dott image December, 2024

AnnoGCD: a generalized category discovery framework for automatic cell type annotation

The identification of cell types in single-cell RNA sequencing (scRNA-seq) data is a critical task in understanding complex biological systems. Traditional supervised machine learning method...

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