S. Vasilʼev

9.9k citations
2 papers · 8 · h-index 2

Impact in

    • Machine Learning in Bioinformatics
    • Fractal and DNA sequence analysis
    • Genomics and Phylogenetic Studies
    • Protein Structure and Dynamics
    • Genetics, Bioinformatics, and Biomedical Research
    • RNA and protein synthesis mechanisms
    • Particle Detector Development and Performance

Papers in

    • Machine Learning in Bioinformatics 1
    • Protein Structure and Dynamics 1
    • Mass Spectrometry Techniques and Applications 1
Journals
Cybernetics and Systems Analysis (1 paper)CERN Bulletin (1 paper)
Partner nations
Ukraine

In The Last Decade

S. Vasilʼev

1 paper receiving 7 citations

Peers

S. Vasilʼev
Comparison fields: 5 of 7
  • Molecular Biology 6
  • Nuclear and High Energy Physics 1
  • Ocean Engineering 1
  • Spectroscopy 1
  • Artificial Intelligence 1
Replace F. Marini with:
F. Marini Italy
Érica Mendonça Portugal
Cary Pennington United States
Otto L. Mohr Hungary
Anthony Attwood United Kingdom
Kanan Lathia United States
Paolo Lorenzi Italy
Antonella Fornaro Italy
Alexander Osborne United Kingdom
Manuel Sanchez United States
S. Vasilʼev relative to F. Marini Italy F. Marini's profile →
Citations per field
00.5×1.5×
F. Marini · 1×
Citations per year

Countries citing papers authored by S. Vasilʼev

Since Specialization
Citations

This map shows the geographic impact of S. Vasilʼev's research. 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 S. Vasilʼev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Vasilʼev more than expected).

Fields of papers citing papers by S. Vasilʼev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Vasilʼev. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by S. Vasilʼev. The network helps show where S. Vasilʼev may publish in the future.

Co-authors

The 3 scholars most cited alongside S. Vasilʼev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S. Vasilʼev Line = papers co-authored together S. Vasilʼev links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown
#Work
1 20077
2 19991

About S. Vasilʼev

S. Vasilʼev is a scholar working on Molecular Biology, Spectroscopy, Nuclear and High Energy Physics, Infectious Diseases and Organic Chemistry, having authored 2 papers that have together received 8 indexed citations. Recurring topics across this work include Dark Matter and Cosmic Phenomena (1 paper), Machine Learning in Bioinformatics (1 paper), Particle Detector Development and Performance (1 paper), Particle physics theoretical and experimental studies (1 paper), Mass Spectrometry Techniques and Applications (1 paper) and Protein Structure and Dynamics (1 paper). The work is most often cited by research in Molecular Biology (6 citations), Nuclear and High Energy Physics (1 citation), Ocean Engineering (1 citation), Spectroscopy (1 citation) and Artificial Intelligence (1 citation). S. Vasilʼev has collaborated with scholars based in Ukraine. Frequent co-authors include И. В. Сергиенко, V. Vagnoni and H. Kolanoski. Their work appears in journals such as Cybernetics and Systems Analysis and CERN Bulletin.

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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