Daniel Himmelstein
Impact in
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- Computational Drug Discovery Methods
- Molecular Biology top 10%
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Biomedical Text Mining and Ontologies
- Gene Regulatory Network Analysis
- Machine Learning in Bioinformatics
Papers in
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- scientometrics and bibliometrics research 3
- Co-authors
- Casey S. GreeneSergio E. BaranziniRan ZhangAaron K. WongGarret A. FitzGeraldDaniel I. ChasmanEmanuela RicciottiTilo Großer
- Journals
- BioData Mining (2 papers)PLoS Computational Biology (2 papers)eLife (2 papers)Scientific Data (2 papers)GigaScience (2 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Daniel Himmelstein
46 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 146
- Computational Theory and Mathematics 281
- Molecular Biology 989
- Health Informatics 16
- Statistics, Probability and Uncertainty 81
- Genetics 286
Countries citing papers authored by Daniel Himmelstein
This map shows the geographic impact of Daniel Himmelstein'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 Daniel Himmelstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Himmelstein more than expected).
Fields of papers citing papers by Daniel Himmelstein
This network shows the impact of papers produced by Daniel Himmelstein. 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 Daniel Himmelstein. The network helps show where Daniel Himmelstein may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Himmelstein, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 8 | |
| 2 | 2022 | 6 | |
| 3 | 2022 | 14 | |
| 4 | 2021 | 7 | |
| 5 | 2020 | 45 | |
| 6 | 2020 | 36 | |
| 7 | PMLB v1.0: an open source dataset collection for benchmarking machine learning methods | 2020 | 0 |
| 8 | 2019 | 40 | |
| 9 | 2017 | 305 | |
| 10 | 2017 | 1 | |
| 11 | 2017 | 29 | |
| 12 | 2016 | 1 | |
| 13 | 2016 | 7 | |
| 14 | 2016 | 3 | |
| 15 | 2016 | 1 | |
| 16 | 2016 | 1 | |
| 17 | Understanding multicellular function and disease with human tissue-specific networks Hit paper breakdown → | 2015 | 562 |
| 18 | 2015 | 2 | |
| 19 | 2011 | 20 | |
| 20 | 2009 | 42 |
About Daniel Himmelstein
Daniel Himmelstein is a scholar working on Information Systems and Management, Statistics, Probability and Uncertainty, General Social Sciences, Molecular Biology and Genetics, having authored 49 papers that have together received 1.5k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (11 papers), Bioinformatics and Genomic Networks (11 papers), Gene expression and cancer classification (7 papers), Genetic Associations and Epidemiology (5 papers), Genomics and Rare Diseases (3 papers), scientometrics and bibliometrics research (3 papers), Research Data Management Practices (3 papers) and Semantic Web and Ontologies (3 papers). The work is most often cited by research in Computational Theory and Mathematics (281 citations), Molecular Biology (989 citations), Health Informatics (16 citations), Statistics, Probability and Uncertainty (81 citations) and Genetics (286 citations). Daniel Himmelstein has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Casey S. Greene, Sergio E. Baranzini, Ran Zhang, Aaron K. Wong, Garret A. FitzGerald, Daniel I. Chasman, Emanuela Ricciotti, Tilo Großer, Antoine Lizée and Kara Dolinski. Their work appears in journals such as BioData Mining, PLoS Computational Biology, eLife, Scientific Data and GigaScience.
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.