Justin Reese

10.6k total citations
44 papers, 1.3k citations indexed

About

Justin Reese is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Justin Reese has authored 44 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 19 papers in Genetics and 11 papers in Artificial Intelligence. Recurrent topics in Justin Reese's work include Biomedical Text Mining and Ontologies (10 papers), Genomics and Rare Diseases (8 papers) and Genomics and Phylogenetic Studies (7 papers). Justin Reese is often cited by papers focused on Biomedical Text Mining and Ontologies (10 papers), Genomics and Rare Diseases (8 papers) and Genomics and Phylogenetic Studies (7 papers). Justin Reese collaborates with scholars based in United States, Italy and Germany. Justin Reese's co-authors include Christine G. Elsik, Natalia V. Milshina, Christopher Childers, Mónica Muñoz-Torres, Aaron J. Mackey, George M. Weinstock, David S. Roos, Kevin L. Childs, Peter N. Robinson and Gregg Helt and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Justin Reese

44 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Justin Reese United States	18	663	410	274	215	138	44	1.3k
James B. Munro United States	20	732 1.1×	319 0.8×	402 1.5×	121 0.6×	467 3.4×	33	1.8k
Mónica Muñoz-Torres United States	10	762 1.1×	265 0.6×	129 0.5×	398 1.9×	87 0.6×	19	1.2k
Claire Lemaitre France	21	576 0.9×	287 0.7×	304 1.1×	306 1.4×	68 0.5×	46	1.1k
René L. Warren Canada	21	1.4k 2.2×	374 0.9×	114 0.4×	510 2.4×	130 0.9×	65	2.0k
Marie‐France Sagot France	21	712 1.1×	221 0.5×	411 1.5×	324 1.5×	66 0.5×	53	1.4k
Nomi L. Harris United States	18	1.7k 2.5×	494 1.2×	64 0.2×	304 1.4×	88 0.6×	44	2.4k
Colin Diesh United States	9	678 1.0×	305 0.7×	99 0.4×	304 1.4×	69 0.5×	13	1.0k
Liang Tang China	17	359 0.5×	244 0.6×	76 0.3×	176 0.8×	149 1.1×	103	861
Bernard Jacq France	15	1.6k 2.4×	235 0.6×	117 0.4×	384 1.8×	128 0.9×	22	2.1k

Countries citing papers authored by Justin Reese

Since Specialization

Citations

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

Fields of papers citing papers by Justin Reese

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Justin Reese

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

All Works

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20 of 20 papers shown

Soto, Mauricio, Carlos Cano, Justin Reese, et al.. (2025). Biasing second-order random walk sampling for heterogeneous graph embedding ^*. 1–8. 1 indexed citations

Chan, Lauren, Elena Casiraghi, Justin Reese, et al.. (2024). Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests. International Journal of Medical Informatics. 187. 105461–105461. 4 indexed citations

Cappelletti, Luca, Tommaso Fontana, Elena Casiraghi, et al.. (2024). Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning. Bioinformatics Advances. 4(1). vbae036–vbae036. 4 indexed citations

Coleman, Ben, Elena Casiraghi, Tiffany J. Callahan, et al.. (2024). Association of post-COVID phenotypic manifestations with new-onset psychiatric disease. Translational Psychiatry. 14(1). 246–246. 1 indexed citations

Caufield, J. Harry, Harshad Hegde, Vincent Emonet, et al.. (2024). Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning. Bioinformatics. 40(3). 31 indexed citations

Gliozzo, Jessica, Mauricio Soto, Valentina Guarino, et al.. (2024). Intrinsic-dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing. Artificial Intelligence in Medicine. 160. 103049–103049. 6 indexed citations

Soto, Mauricio, Paolo Perlasca, Jessica Gliozzo, et al.. (2024). An ontology-based knowledge graph for representing interactions involving RNA molecules. Scientific Data. 11(1). 906–906. 7 indexed citations

Groza, Tudor, Gareth Baynam, Melissa Haendel, et al.. (2024). An evaluation of GPT models for phenotype concept recognition. BMC Medical Informatics and Decision Making. 24(1). 30–30. 14 indexed citations

Valentini, Giorgio, Dario Malchiodi, Jessica Gliozzo, et al.. (2023). The promises of large language models for protein design and modeling. SHILAP Revista de lepidopterología. 3. 1304099–1304099. 18 indexed citations

10.

Blau, Hannah, Elena Casiraghi, Johanna Loomba, et al.. (2023). Predictive models of long COVID. EBioMedicine. 96. 104777–104777. 18 indexed citations

11.

Ravanmehr, Vida, Hannah Blau, Luca Cappelletti, et al.. (2021). Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer. NAR Genomics and Bioinformatics. 3(4). lqab113–lqab113. 4 indexed citations

12.

Casiraghi, Elena, Dario Malchiodi, Gabriella Trucco, et al.. (2020). Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE Access. 8. 196299–196325. 56 indexed citations

13.

Reese, Justin, et al.. (2020). Design, Construction, And Commissioning Of A 60 Kw Microturbine Demonstration Facility. 8.377.1–8.377.7. 1 indexed citations

14.

Robinson, Peter N., Vida Ravanmehr, Julius O.B. Jacobsen, et al.. (2020). Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. The American Journal of Human Genetics. 107(3). 403–417. 51 indexed citations

15.

Boonsawat, Watchara, et al.. (2018). Oxidative Stress and Inflammation after Continuous Positive Airway Pressure Therapy in Patients with Severe Obstructive Sleep Apnea. 275–282. 2 indexed citations

16.

Hunter, Wayne B. & Justin Reese. (2014). The Asian Citrus Psyllid Genome (Diaphorina citri, Hemiptera). 1(1). 9 indexed citations

17.

Elsik, Christine G., Aaron J. Mackey, Justin Reese, et al.. (2007). Creating a honey bee consensus gene set. Genome biology. 8(1). R13–R13. 184 indexed citations

18.

Babin–Ebell, Jörg, et al.. (2007). Serum S100B Levels in Patients after Cardiac Surgery: Possible Sources of Contamination. The Thoracic and Cardiovascular Surgeon. 55(3). 168–172. 6 indexed citations

19.

Elsik, Christine G., Kim C. Worley, Lan Zhang, et al.. (2006). Community annotation: Procedures, protocols, and supporting tools: Table 1.. Genome Research. 16(11). 1329–1333. 36 indexed citations

20.

Reese, Justin, et al.. (2001). Downregulated Expression of Ly‐6‐ThB on DevelopingT Cells Marks CD4⁺CD8⁺ Subset Undergoing Selectionin the Thymus. Journal of Immunology Research. 8(2). 107–121. 7 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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