Maxwell L. Bileschi

4.4k citations
9 papers · 593 indexed · 1 hit paper · h-index 7
Topics
Machine Learning in Bioinformatics (6 papers)Genomics and Phylogenetic Studies (4 papers)Protein Structure and Dynamics (2 papers)

In The Last Decade

Maxwell L. Bileschi

9 papers receiving 581 citations

Hit Papers

Using deep learning to annotate the protein universe2022202620232024202250100150

Peers

Maxwell L. Bileschi
Comparison fields: 5 of 69
  • Molecular Biology 472
  • Genetics 71
  • Plant Science 46
  • Computational Theory and Mathematics 42
  • Artificial Intelligence 39
Replace Chaoyang Zhang with:
Chaoyang Zhang United States
Saad Haider United States
Aleksandra Gruca Poland
Adam J. Riesselman United States
Morteza Mohammad-Noori Iran
Joana P. Gonçalves Portugal
Xuefeng Cui China
Jiří Vohradský Czechia
Matthew D. Edwards United States
Laurent Tournier France
Maxwell L. Bileschi relative to Chaoyang Zhang United States Chaoyang Zhang's profile →
Citations per field
00.5×3.9×
Chaoyang Zhang · 1×
Citations per year

Countries citing papers authored by Maxwell L. Bileschi

Since Specialization
Citations

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

Fields of papers citing papers by Maxwell L. Bileschi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maxwell L. Bileschi. 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 Maxwell L. Bileschi. The network helps show where Maxwell L. Bileschi may publish in the future.

Co-authorship network of co-authors of Maxwell L. Bileschi

This figure shows the co-authorship network connecting the top 25 collaborators of Maxwell L. Bileschi. A scholar is included among the top collaborators of Maxwell L. Bileschi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maxwell L. Bileschi. Maxwell L. Bileschi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
#WorkIndexed citations
1 49
2 1
3 82
4
Using deep learning to annotate the protein universebreakdown →
164
5 6
6 6
7 264
8 9
9 12

About Maxwell L. Bileschi

Maxwell L. Bileschi is a scholar working on Discrete Mathematics and Combinatorics, Spectroscopy and Molecular Biology, having authored 9 papers that have together received 593 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (6 papers), Genomics and Phylogenetic Studies (4 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Molecular Biology (472 citations), Computational Theory and Mathematics (42 citations) and Genetics (71 citations). Maxwell L. Bileschi has collaborated with scholars based in United States, United Kingdom and Sweden. Frequent co-authors include David Belanger, Lucy J. Colwell, Cory Y. McLean, Yakir Reshef, David R. Kelley, Jasper Snoek, David Belanger, Theo Sanderson, Alex Bateman and Brandon Carter. Their work appears in journals such as Nucleic Acids Research, Nature Biotechnology and Genome Research.

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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