Journal profile

Interdisciplinary sciences, computational life sciences

Publisher: Springer-Verlag
ISSN1913-2751 | 1867-1462 FormatPrint StatusCurrently indexed

Journal Citation Impact

Estimated influence, citation density, publishing volume, and citation integrity from the analytics table.

Impact Factor
3.95
Citation rate 5.91 754 total citations
h5-index 32 h5 depth signal
Cited share 91.7% 122 of 133
Total citations 754 Citation footprint
Metric signal 3.95 External metric anchor
Self-citation 5.6% 42 self-citations

Citations Trend

Monthly Publications Cadence

Citing Geographic Demographics

China 499
India 127
United States 45
Turkey 17
Pakistan 17

Citing Fields of Research

AI & Machine Learning 269
Genetics & Genomics 252
Protein Structure & Folding 142
Noncoding RNA 75
Computer Vision & Graphics 59

Publishing Activity & Reach

Volume, recency, access pattern, and citing-community signals from the journal analytics record.

All-time article footprint 1,071 Articles tracked across the journal lifetime
Indexed publication set 133 122 cited publications
Last 12 months 107 8.9 publications/month
Open access share 3.7% 4 OA publications in the last year
Leading citing country China 499 citing publications
Leading citing field AI & Machine Learning 269 citing publications
Latest tracked publication 16 October 2026 10 recent records available

Coverage & Indexing History

MEDLINE
v1n1, Mar. 2009-
PubMed
v1n1, Mar. 2009-

Research Focus & Classification

Indexed Subjects

AI & Machine Learning Genetics & Genomics Protein Structure & Folding Noncoding RNA

Issuing & Sponsoring Bodies

Contributor
International Association of Scientists in the Interdisciplinary Area

Broad Categories

Computational Biology Genetics & Genomics Protein Structure & Folding Noncoding RNA Computer Vision & Graphics Nucleic Acids Chemistry

MeSH Descriptors

Biological Science Disciplines Computational Biology Computer-Aided Design

Latest Publications

Recent article records with DOI-first linking and condensed abstracts.

02
Binhua Tang, Yingying Feng, Guowei Cheng et al.
Self-supervised and context-aware representation learning offers a powerful strategy for extracting robust features from high-dimensional, sparse, and noisy single-cell data. Here, we present scDGCN, a graph-based deep clustering framewo...

Bibliographical & Catalog Notes

Description based on: Vol. 2, no. 2 (June 2010); title from cover.

Latest issue consulted: Vol. 2, no. 2 (June 2010).

Also issued online.