Melissa Haendel

24.1k total citations · 3 hit papers
165 papers, 4.9k citations indexed

About

Melissa Haendel is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Melissa Haendel has authored 165 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Molecular Biology, 51 papers in Artificial Intelligence and 41 papers in Genetics. Recurrent topics in Melissa Haendel's work include Biomedical Text Mining and Ontologies (76 papers), Semantic Web and Ontologies (36 papers) and Genomics and Rare Diseases (32 papers). Melissa Haendel is often cited by papers focused on Biomedical Text Mining and Ontologies (76 papers), Semantic Web and Ontologies (36 papers) and Genomics and Rare Diseases (32 papers). Melissa Haendel collaborates with scholars based in United States, United Kingdom and Germany. Melissa Haendel's co-authors include Chris Mungall, Peter N. Robinson, Suzanna Lewis, Georgios V. Gkoutos, Nicole Vasilevsky, Carlo Torniai, Christopher G. Chute, Nicole Washington, Michael Ashburner and Monte Westerfield and has published in prestigious journals such as New England Journal of Medicine, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Melissa Haendel

153 papers receiving 4.8k 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.

Uberon, an integrative multi-species anatomy ontology

2012 441 citations Chris Mungall, Melissa Haendel et al. profile →
How many rare diseases are there?

2019 279 citations Melissa Haendel, Nicole Vasilevsky et al. profile →
Coding long COVID: characterizing a new disease through an ICD-10 lens

2023 84 citations Emily Pfaff, Charisse Madlock‐Brown et al. profile →

Peers

Melissa Haendel

GENET

Nils Gehlenborg United States

Casey S. Greene United States

Helen Parkinson United Kingdom

Alexander Lex United States

Jonathan D. Wren United States

Sven Rahmann Germany

Morris A. Swertz Netherlands

Lawrence Hunter United States

Georgios V. Gkoutos United Kingdom

Won Bae Kim South Korea

Nils Gehlenborg United States

Melissa Haendel

3.2k ×1.1

598 ×0.5

GENET

456 ×0.5

488 ×1.7

155 ×0.6

Citations per year, relative to Melissa Haendel Melissa Haendel (= 1×) peers Nils Gehlenborg

Countries citing papers authored by Melissa Haendel

Since Specialization

Citations

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

Fields of papers citing papers by Melissa Haendel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Melissa Haendel

This figure shows the co-authorship network connecting the top 25 collaborators of Melissa Haendel. A scholar is included among the top collaborators of Melissa Haendel 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 Melissa Haendel. Melissa Haendel 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

Kahn, Michael G., Tellen D. Bennett, Rachel Deer, et al.. (2025). Identifying who has long COVID in the USA: a machine learning approach using N3C data. UNC Libraries.

Powers, John, Abhishek Bhatia, Charisse Madlock‐Brown, et al.. (2025). Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohort. Communications Medicine. 5(1). 109–109. 1 indexed citations

Hurwitz, Eric, et al.. (2025). Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study. JMIR Formative Research. 9. e67585–e67585.

Preiss, Alexander, M. Daniel Brannock, John M. Baratta, et al.. (2025). Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia. The Lancet Digital Health. 7(8). 100887–100887. 1 indexed citations

Hurwitz, Eric, Cara D Varley, Alfred Anzalone, et al.. (2025). Identifying People Living With or Those at Risk for HIV in a Nationally Sampled Electronic Health Record Repository Called the National Clinical Cohort Collaborative: Computational Phenotyping Study. JMIR Medical Informatics. 13. e68143–e68143. 1 indexed citations

O’Neil, Shawn T., Charisse Madlock‐Brown, Kenneth J. Wilkins, et al.. (2024). Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs. npj Digital Medicine. 7(1). 296–296. 2 indexed citations

Jacobsen, Julius O.B., Susan Walker, Valentina Cipriani, et al.. (2024). Efficient reinterpretation of rare disease cases using Exomiser. npj Genomic Medicine. 9(1). 65–65. 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

Martin, Blake, Peter E. DeWitt, Seth Russell, et al.. (2024). The Recent Increase in Invasive Bacterial Infections: A Report From the National COVID Cohort Collaborative. The Pediatric Infectious Disease Journal. 44(3). 217–227.

10.

Thaxton, Courtney, Leslie G. Biesecker, Marina T. DiStefano, et al.. (2024). Implementation of a dyadic nomenclature for monogenic diseases. The American Journal of Human Genetics. 111(9). 1810–1818. 6 indexed citations

11.

Klein, Klara R., Anna R. Kahkoska, G. Caleb Alexander, et al.. (2024). Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity. Diabetes Therapy. 15(5). 1169–1186. 1 indexed citations

12.

Suver, Christine, Jeremy Harper, Johanna Loomba, et al.. (2023). The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale. Journal of Clinical and Translational Science. 7(1). e252–e252. 3 indexed citations

13.

Hill, Elaine, Hemalkumar B. Mehta, Suchetha Sharma, et al.. (2023). Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health. 23(1). 2103–2103. 25 indexed citations

14.

Levitt, Eli, David A. Patch, Melissa Haendel, et al.. (2022). Ankle Fracture and Length of Stay in US Adult Population Using Data From the National COVID Cohort Collaborative. Foot & Ankle Orthopaedics. 7(1). 1768354594–1768354594. 3 indexed citations

15.

Thaxton, Courtney, Jennifer Goldstein, Marina T. DiStefano, et al.. (2022). Lumping versus splitting: How to approach defining a disease to enable accurate genomic curation. Cell Genomics. 2(5). 100131–100131. 17 indexed citations

16.

Jacobsen, Julius O.B., Christèle du Souich, Kent Shefchek, et al.. (2022). The Clinical Variant Analysis Tool: Analyzing the evidence supporting reported genomic variation in clinical practice. Genetics in Medicine. 24(7). 1512–1522. 3 indexed citations

17.

Levitt, Eli, David A. Patch, Byron C. Jaeger, et al.. (2022). Association Between COVID-19 and Mortality in Hip Fracture Surgery in the National COVID Cohort Collaborative (N3C): A Retrospective Cohort Study. JAAOS Global Research and Reviews. 6(1). 20 indexed citations

18.

Vasilevsky, Nicole, Mohammad Hosseini, Ehsan Mohammadi, et al.. (2020). Is authorship sufficient for today’s collaborative research? A call for contributor roles. Accountability in Research. 28(1). 23–43. 45 indexed citations

19.

Tcheandjieu, Catherine, Matthew Aguirre, Stefan Gustafsson, et al.. (2020). A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population. PLoS Genetics. 16(11). e1008802–e1008802. 8 indexed citations

20.

Smedley, Damian, Julius O.B. Jacobsen, Marten Jäger, et al.. (2015). Next-generation diagnostics and disease-gene discovery with the Exomiser. Nature Protocols. 10(12). 2004–2015. 228 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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