Computer applications in the biosciences

115.0k citations
1.4k papers · · active since 1951

Impact in

    • Genomics and Phylogenetic Studies
    • RNA and protein synthesis mechanisms
    • Machine Learning in Bioinformatics
    • Bioinformatics and Genomic Networks
    • Protein Structure and Dynamics
    • Gene expression and cancer classification
    • Genomics and Chromatin Dynamics
  • Genetics top 5%
    • Genetic diversity and population structure

Papers in

    • Genomics and Phylogenetic Studies 404
    • RNA and protein synthesis mechanisms 356
    • Machine Learning in Bioinformatics 252
    • Gene expression and cancer classification 233
    • Protein Structure and Dynamics 181
    • Bioinformatics and Genomic Networks 158
    • Genetics, Bioinformatics, and Biomedical Research 90
    • Algorithms and Data Compression 132

Computer applications in the biosciences

1.3k papers receiving 108.4k citations

Peers

Computer applications in the biosciences
Comparison fields: 5 of 239
  • Molecular Biology 71.7k
  • Genetics 18.3k
  • Plant Science 22.3k
  • Endocrinology 2.3k
  • Ecology 10.9k
Replace Microbiome with:
Microbiome United States
Journal of Statistical Software United States
Nature Microbiology United States
Cladistics United States
FEMS Microbiology Reviews United States
Annual Review of Genetics United States
Microbiology and Molecular Biology Reviews United States
Annual Review of Cell and Developmental Biology United States
Cold Spring Harbor Perspectives in Biology United States
Molecular Systems Biology United States
Computer applications in the biosciences relative to Microbiome United States Microbiome's profile →
Citations per field
00.5×2.5×
Microbiome · 1×
Citations per year

Countries where authors publish in Computer applications in the biosciences

Since Specialization
Citations

This map shows the geographic impact of research published in Computer applications in the biosciences. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Computer applications in the biosciences with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computer applications in the biosciences more than expected).

Fields of papers published in Computer applications in the biosciences

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Computer applications in the biosciences. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Computer applications in the biosciences.

About Computer applications in the biosciences

The 1.4k papers published in Computer applications in the biosciences in the last decades have received a total of 115.0k indexed citations . Papers published in Computer applications in the biosciences usually cover Molecular Biology (1.1k papers), Artificial Intelligence (196 papers), Spectroscopy (93 papers), Genetics (152 papers) and Information Systems and Management (34 papers) specifically the topics of Genomics and Phylogenetic Studies (404 papers), RNA and protein synthesis mechanisms (356 papers), Machine Learning in Bioinformatics (252 papers), Gene expression and cancer classification (233 papers), Protein Structure and Dynamics (181 papers), Bioinformatics and Genomic Networks (158 papers), Algorithms and Data Compression (132 papers) and Genetics, Bioinformatics, and Biomedical Research (90 papers). The most active scholars publishing in Computer applications in the biosciences are Roderic Page, William R. Taylor, Janet M. Thornton, Kebin Liu, Spencer V. Muse, Desmond G. Higgins, Yves Van de Peer, Rupert De Wächter, Andrew Rambaut and Steven Maere.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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