Algorithms for Molecular Biology

11.4k citations
450 papers · · active since 2016

Impact in

    • RNA and protein synthesis mechanisms
    • Genomics and Phylogenetic Studies
    • RNA modifications and cancer
    • RNA Research and Splicing
    • Bioinformatics and Genomic Networks
    • Gene expression and cancer classification
    • Machine Learning in Bioinformatics

Papers in

    • Genomics and Phylogenetic Studies 113
    • RNA and protein synthesis mechanisms 50
    • Gene expression and cancer classification 34
    • Bioinformatics and Genomic Networks 29
    • Machine Learning in Bioinformatics 19
    • Genome Rearrangement Algorithms 41

Algorithms for Molecular Biology

320 papers receiving 8.6k citations

Peers

Algorithms for Molecular Biology
Comparison fields: 5 of 192
  • Molecular Biology 8.2k
  • Cancer Research 972
  • Genetics 1.6k
  • Artificial Intelligence 1.2k
  • Plant Science 1.1k
Replace Statistical Applications in Genetics and Molecular Biology with:
Statistical Applications in Genetics and Molecular Biology United States
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Theoretical Biology and Medical Modelling United States
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Bioinformatics and Biology Insights United States
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Algorithms for Molecular Biology relative to Statistical Applications in Genetics and Molecular Biology United States Statistical Applications in Genetics and Molecular Biology's profile →
Citations per field
00.5×1.5×
Statistical Applications in Genetics and Molecular Biology · 1×
Citations per year

Countries where authors publish in Algorithms for Molecular Biology

Since Specialization
Citations

This map shows the geographic impact of research published in Algorithms for Molecular Biology. 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 Algorithms for Molecular Biology with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Algorithms for Molecular Biology more than expected).

Fields of papers published in Algorithms for Molecular Biology

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Algorithms for Molecular Biology. 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 Algorithms for Molecular Biology.

About Algorithms for Molecular Biology

The 450 papers published in Algorithms for Molecular Biology in the last decades have received a total of 11.4k indexed citations . Papers published in Algorithms for Molecular Biology usually cover Molecular Biology (250 papers), Genetics (77 papers), Artificial Intelligence (56 papers), Computational Theory and Mathematics (15 papers) and Cancer Research (11 papers) specifically the topics of Genomics and Phylogenetic Studies (113 papers), Algorithms and Data Compression (52 papers), RNA and protein synthesis mechanisms (50 papers), Genome Rearrangement Algorithms (41 papers), Gene expression and cancer classification (34 papers), Bioinformatics and Genomic Networks (29 papers), Chromosomal and Genetic Variations (22 papers) and Machine Learning in Bioinformatics (19 papers). The most active scholars publishing in Algorithms for Molecular Biology are Peter Florian Stadler, Ivo L. Hofacker, Christoph Flamm, Hakim Tafer, Ronny Lorenz, Christian Höner zu Siederdissen, Guillaume Rizk, Rayan Chikhi, Burkhard Morgenstern and Ran Libeskind-Hadas.

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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