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Journal Photo for Bioinformatics
Peer reviewed only Open Access

Bioinformatics (Bioinformatics)

Publisher : Oxford University Press
Mathematics Statistics Computational Mathematics
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e-ISSN 1367-4811
p-ISSN 1367-4803
Issue Frequency Bi-Monthly
Impact Factor 5.8
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 P-ISSN: 1367-4803, E-ISSN: 1367-4811, Established: 1998, Impact Factor: 5.8
  • Does Not Provide Crossref DOI

  • Indexed in: Scopus, DOAJ, PubMed

  • Not indexed in WoS, UGC CARE

Indexing

Publications of Bioinformatics

CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
Pietro Liò March, 2022
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 ...
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases
Pietro Liò March, 2022
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...
Adversarial generation of gene expression data
Pietro Liò January, 2021
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...