Luke Sheneman

459 total citations
11 papers, 315 citations indexed

About

Luke Sheneman is a scholar working on Molecular Biology, Artificial Intelligence and Biophysics. According to data from OpenAlex, Luke Sheneman has authored 11 papers receiving a total of 315 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Artificial Intelligence and 3 papers in Biophysics. Recurrent topics in Luke Sheneman's work include Genomics and Phylogenetic Studies (5 papers), Advanced Fluorescence Microscopy Techniques (3 papers) and Genetic diversity and population structure (2 papers). Luke Sheneman is often cited by papers focused on Genomics and Phylogenetic Studies (5 papers), Advanced Fluorescence Microscopy Techniques (3 papers) and Genetic diversity and population structure (2 papers). Luke Sheneman collaborates with scholars based in United States. Luke Sheneman's co-authors include James A. Foster, Conrad Shyu, Andreas E. Vasdekis, Gregory Stephanopoulos, Michael Barnett, Donna Delparte, Sajjad Ahmad, S. W. Tyler, Rui Wu and Matthew A. Turner and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and PLoS ONE.

In The Last Decade

Luke Sheneman

10 papers receiving 303 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Luke Sheneman United States	4	204	77	37	30	23	11	315
David Danko United States	10	234 1.1×	116 1.5×	29 0.8×	50 1.7×	25 1.1×	23	399
Sergio Coronado Luxembourg	3	174 0.9×	108 1.4×	22 0.6×	14 0.5×	30 1.3×	6	338
Binghang Liu China	5	305 1.5×	101 1.3×	69 1.9×	64 2.1×	27 1.2×	9	380
Stephen Woloszynek United States	11	166 0.8×	59 0.8×	18 0.5×	17 0.6×	36 1.6×	13	339
M. S. Binoj Kumar United States	6	243 1.2×	157 2.0×	18 0.5×	29 1.0×	16 0.7×	12	340
Xin‐Yi Chua Australia	5	152 0.7×	39 0.5×	23 0.6×	18 0.6×	12 0.5×	13	224
Clara Amid United Kingdom	8	156 0.8×	69 0.9×	29 0.8×	14 0.5×	23 1.0×	11	259
Veronika Dubinkina United States	8	202 1.0×	69 0.9×	53 1.4×	16 0.5×	16 0.7×	16	306
Hinako Tamotsu Japan	8	164 0.8×	83 1.1×	28 0.8×	55 1.8×	18 0.8×	17	292
Monika Balvočiūtė Germany	4	232 1.1×	126 1.6×	44 1.2×	24 0.8×	36 1.6×	5	359

Countries citing papers authored by Luke Sheneman

Since Specialization

Citations

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

Fields of papers citing papers by Luke Sheneman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luke Sheneman

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Sheneman, Luke, et al.. (2025). Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films. AI. 6(3). 60–60. 2 indexed citations

2.

Sheneman, Luke, et al.. (2025). Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy. Proceedings of the National Academy of Sciences. 122(21). e2412261122–e2412261122.

3.

Sheneman, Luke, Gregory Stephanopoulos, & Andreas E. Vasdekis. (2021). Deep learning classification of lipid droplets in quantitative phase images. PLoS ONE. 16(4). e0249196–e0249196. 13 indexed citations

4.

Sheneman, Luke, et al.. (2021). Photon-Sparse, Poisson Light-Sheet Microscopy. ACS Photonics. 8(10). 2876–2881. 3 indexed citations

5.

Chen, Chao, Rui Wu, Sajjad Ahmad, et al.. (2020). Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management. Frontiers in Water. 2. 3 indexed citations

6.

Sheneman, Luke, et al.. (2006). Relaxed Neighbor Joining: A Fast Distance-Based Phylogenetic Tree Construction Method. Journal of Molecular Evolution. 62(6). 785–792. 110 indexed citations

7.

Sheneman, Luke & James A. Foster. (2006). Estimating the destructiveness of crossover on binary tree representations. 1427–1428. 2 indexed citations

8.

Sheneman, Luke, et al.. (2006). Clearcut: a fast implementation of relaxed neighbor joining. Bioinformatics. 22(22). 2823–2824. 148 indexed citations

9.

Shyu, Conrad, Luke Sheneman, & James A. Foster. (2004). Multiple Sequence Alignment with Evolutionary Computation. Genetic Programming and Evolvable Machines. 5(2). 121–144. 30 indexed citations

10.

Sheneman, Luke & James A. Foster. (2003). Evolving Guide Trees in Progressive Multiple Sequence Alignment. 1 indexed citations

11.

Foster, James A., et al.. (1995). (In)Formal Methods: Teaching Program Derivation Via the Moore Method. Computer Science Education. 6(1). 67–91. 3 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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