Maxwell L. Bileschi

4.4k total citations · 1 hit paper
9 papers, 593 citations indexed

About

Maxwell L. Bileschi is a scholar working on Molecular Biology, Spectroscopy and Discrete Mathematics and Combinatorics. According to data from OpenAlex, Maxwell L. Bileschi has authored 9 papers receiving a total of 593 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 2 papers in Spectroscopy and 1 paper in Discrete Mathematics and Combinatorics. Recurrent topics in Maxwell L. Bileschi's work include Machine Learning in Bioinformatics (6 papers), Genomics and Phylogenetic Studies (4 papers) and Protein Structure and Dynamics (2 papers). Maxwell L. Bileschi is often cited by papers focused on Machine Learning in Bioinformatics (6 papers), Genomics and Phylogenetic Studies (4 papers) and Protein Structure and Dynamics (2 papers). Maxwell L. Bileschi collaborates with scholars based in United States, United Kingdom and Sweden. Maxwell L. Bileschi's 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 and has published in prestigious journals such as Nucleic Acids Research, Nature Biotechnology and Genome Research.

In The Last Decade

Maxwell L. Bileschi

9 papers receiving 581 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Using deep learning to annotate the protein universe

2022 164 citations Maxwell L. Bileschi, David Belanger et al. Nature Biotechnology profile →

Peers

Maxwell L. Bileschi

MB

GENET

PS

CTM

AI

Chaoyang Zhang United States

Aleksandra Gruca Poland

Matthew D. Edwards United States

Mikhail Roytberg Russia

Bryce Kille United States

Ramzan Umarov Saudi Arabia

Dongyu Xue China

Gershon Celniker Israel

Karen Christie United States

James Vlasblom Canada

Chaoyang Zhang United States

Maxwell L. Bileschi

496 ×1.1

MB

73 ×1.0

GENET

46 ×1.0

PS

38 ×0.9

CTM

68 ×1.7

AI

Citations per year, relative to Maxwell L. Bileschi Maxwell L. Bileschi (= 1×) peers Chaoyang Zhang

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

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9 of 9 papers shown

1.

Paysan‐Lafosse, Typhaine, Antonina Andreeva, Matthias Blum, et al.. (2024). The Pfam protein families database: embracing AI/ML. Nucleic Acids Research. 53(D1). D523–D534. 49 indexed citations

2.

Andreeva, Antonina, et al.. (2024). Investigation of protein family relationships with deep learning. Bioinformatics Advances. 4(1). vbae132–vbae132. 1 indexed citations

3.

Sanderson, Theo, Maxwell L. Bileschi, David Belanger, & Lucy J. Colwell. (2023). ProteInfer, deep neural networks for protein functional inference. eLife. 12. 82 indexed citations

4.

Bileschi, Maxwell L., David Belanger, Drew Bryant, et al.. (2022). Using deep learning to annotate the protein universe. Nature Biotechnology. 40(6). 932–937. 164 indexed citations breakdown →

5.

Dohan, David, et al.. (2021). Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights. 2782–2791. 6 indexed citations

6.

Carter, Brandon, Maxwell L. Bileschi, Jamie Smith, et al.. (2019). Critiquing Protein Family Classification Models Using Sufficient Input Subsets. Journal of Computational Biology. 27(8). 1219–1231. 6 indexed citations

7.

Kelley, David R., Yakir Reshef, Maxwell L. Bileschi, et al.. (2018). Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Research. 28(5). 739–750. 264 indexed citations

8.

Bileschi, Maxwell L., et al.. (2013). Security analysis for fingerprint fuzzy vaults. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8712. 871204–871204. 9 indexed citations

9.

Bileschi, Maxwell L., et al.. (2011). Weights of Boolean cubic monomial rotation symmetric functions. Cryptography and Communications. 4(2). 105–130. 12 indexed citations

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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