Taylor Applebaum

2.0k total citations · 1 hit paper
6 papers, 726 citations indexed

About

Taylor Applebaum is a scholar working on Artificial Intelligence, Discrete Mathematics and Combinatorics and Signal Processing. According to data from OpenAlex, Taylor Applebaum has authored 6 papers receiving a total of 726 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Artificial Intelligence, 1 paper in Discrete Mathematics and Combinatorics and 1 paper in Signal Processing. Recurrent topics in Taylor Applebaum's work include Genomics and Rare Diseases (1 paper), Genetic Associations and Epidemiology (1 paper) and Insect symbiosis and bacterial influences (1 paper). Taylor Applebaum is often cited by papers focused on Genomics and Rare Diseases (1 paper), Genetic Associations and Epidemiology (1 paper) and Insect symbiosis and bacterial influences (1 paper). Taylor Applebaum collaborates with scholars based in United Kingdom, United States and Canada. Taylor Applebaum's co-authors include Pushmeet Kohli, Andrew Senior, Alexander Pritzel, Demis Hassabis, Rosalia G. Schneider, Žiga Avsec, Guido Novati, Tobias Sargeant, John Jumper and Lai Hong Wong and has published in prestigious journals such as Science, Applied and Environmental Microbiology and Algebra & Number Theory.

In The Last Decade

Taylor Applebaum

5 papers receiving 714 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.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

2023 692 citations Jun Cheng, Guido Novati et al. Science profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Taylor Applebaum United Kingdom	4	426	280	69	43	39	6	726
Rosalia G. Schneider United Kingdom	3	424 1.0×	275 1.0×	69 1.0×	40 0.9×	39 1.0×	4	734
Lai Hong Wong Canada	9	563 1.3×	320 1.1×	88 1.3×	53 1.2×	63 1.6×	17	898
Xiangqun Zheng-Bradley United Kingdom	9	400 0.9×	275 1.0×	82 1.2×	64 1.5×	35 0.9×	11	719
Michael F. Buckley Australia	15	332 0.8×	227 0.8×	39 0.6×	52 1.2×	40 1.0×	32	701
Tymor Hamamsy United States	6	369 0.9×	289 1.0×	115 1.7×	29 0.7×	54 1.4×	7	671
Alison Meynert United Kingdom	15	403 0.9×	225 0.8×	110 1.6×	28 0.7×	67 1.7×	28	770
Ingrid Lönnstedt Sweden	12	560 1.3×	176 0.6×	66 1.0×	29 0.7×	40 1.0×	17	792
Mary Shimoyama United States	19	717 1.7×	233 0.8×	70 1.0×	39 0.9×	23 0.6×	47	991
Vidhya G. Krishnan Singapore	8	720 1.7×	411 1.5×	113 1.6×	41 1.0×	46 1.2×	12	999
Abhishek Sarkar United States	11	426 1.0×	308 1.1×	75 1.1×	46 1.1×	19 0.5×	19	705

Countries citing papers authored by Taylor Applebaum

Since Specialization

Citations

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

Fields of papers citing papers by Taylor Applebaum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Taylor Applebaum. 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 Taylor Applebaum. The network helps show where Taylor Applebaum may publish in the future.

Co-authorship network of co-authors of Taylor Applebaum

This figure shows the co-authorship network connecting the top 25 collaborators of Taylor Applebaum. A scholar is included among the top collaborators of Taylor Applebaum 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 Taylor Applebaum. Taylor Applebaum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Applebaum, Taylor, et al.. (2023). Constructions of difference sets in nonabelian 2-groups. Algebra & Number Theory. 17(2). 359–396.

2.

Cheng, Jun, Guido Novati, Taylor Applebaum, et al.. (2023). Predictions for AlphaMissense. Zenodo (CERN European Organization for Nuclear Research). 3 indexed citations

3.

Cheng, Jun, Guido Novati, Clare Bycroft, et al.. (2023). Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 381(6664). eadg7492–eadg7492. 692 indexed citations breakdown →

4.

Novati, Guido, Taylor Applebaum, Alexander Pritzel, et al.. (2023). Source code for AlphaMissense. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

5.

Huang, Yiteng, et al.. (2018). Supervised Noise Reduction for Multichannel Keyword Spotting. 5474–5478. 12 indexed citations

6.

Farmer, William R., et al.. (2015). TonB-Dependent Heme Iron Acquisition in the Tsetse Fly Symbiont Sodalis glossinidius. Applied and Environmental Microbiology. 81(8). 2900–2909. 18 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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