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Bioinformatics (Bioinformatics)

Publisher :

Oxford University Press

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Mathematics
  • Statistics
  • Computational Mathematics

    • +3

e-ISSN :

1367-4811

Issue Frequency :

Bi-Monthly

Impact Factor :

5.8

p-ISSN :

1367-4803

Est. Year :

1998

Mobile :

01536452640

Country :

United Kingdom

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

Bioinformatics.editorialoffice@oup.com

Journal Descriptions

The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. One distinct section within the journal - Application Notes - focuses on shorter papers exploring the applications used for experiments.


Bioinformatics (Bioinformatics) is :

International, Peer-Reviewed, Open Access, Refereed, Mathematics, Statistics, Computational Mathematics, Biochemistry, Molecular Biology, phylogenetics , Online or Print, Bi-Monthly Journal

UGC Approved, ISSN Approved: P-ISSN - 1367-4803, E-ISSN - 1367-4811, Established in - 1998, Impact Factor - 5.8

Not Provide Crossref DOI

Indexed in Scopus, DOAJ, PubMed

Not indexed in WoS, UGC CARE

Indexing

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Google Scholar
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Scopus
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DOAJ

+9 More

Publications of Bioinformatics

  • dott image March, 2022

CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks

Motivation Single-cell RNA sequencing allows high-resolution views of individual cells for libraries of up to millions of samples, thus motivating the use of deep learning for analysis. In ...

  • dott image March, 2022

Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases

Motivation Gene expression data are commonly used at the intersection of cancer research and machine learning for better understanding of the molecular status of tumour tissue. Deep learnin...

  • dott image January, 2021

Adversarial generation of gene expression data

Motivation High-throughput gene expression can be used to address a wide range of fundamental biological problems, but datasets of an appropriate size are often unavailable. Moreover, exist...

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