Frank Emmert‐Streib
- Geometry and Topology top 1%
- Graph theory and applications 48
- Health Informatics top 1%
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 31
- Topological and Geometric Data Analysis 12
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- Complex Network Analysis Techniques 47
- Molecular Biology top 5%
- Bioinformatics and Genomic Networks 79
- Gene expression and cancer classification 55
- Gene Regulatory Network Analysis 50
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- Topic Modeling 10
- Co-authors
- Matthias DehmerGalina GlazkoShailesh TripathiGökmen AltayRicardo De Matos SimoesOlli Yli‐HarjaBenjamin Haibe‐KainsYongtang Shi
- Journals
- Nucleic Acids Research (1 paper)SHILAP Revista de lepidopterología (6 papers)Bioinformatics (6 papers)
- Partner nations
- AustriaFinlandUnited States
In The Last Decade
Frank Emmert‐Streib
234 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 215
- Geometry and Topology 610
- Health Informatics 93
- Computational Theory and Mathematics 1.1k
- Statistical and Nonlinear Physics 567
- Molecular Biology 2.4k
Countries citing papers authored by Frank Emmert‐Streib
This map shows the geographic impact of Frank Emmert‐Streib'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 Frank Emmert‐Streib with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frank Emmert‐Streib more than expected).
Fields of papers citing papers by Frank Emmert‐Streib
This network shows the impact of papers produced by Frank Emmert‐Streib. 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 Frank Emmert‐Streib. The network helps show where Frank Emmert‐Streib may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Frank Emmert‐Streib, 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 | 2 | |
| 2 | 2024 | 4 | |
| 3 | 2022 | 7 | |
| 4 | 2021 | 145 | |
| 5 | 2021 | 50 | |
| 6 | 2020 | 16 | |
| 7 | 2019 | 49 | |
| 8 | 2019 | 15 | |
| 9 | 2019 | 13 | |
| 10 | 2019 | 2 | |
| 11 | 2019 | 2 | |
| 12 | 2019 | 6 | |
| 13 | 2017 | 5 | |
| 14 | 2014 | 37 | |
| 15 | 2013 | 4 | |
| 16 | 2012 | 96 | |
| 17 | 2011 | 9 | |
| 18 | Theoretical Bounds for the Number of Inferable Edges in Sparse Random Networks. | 2006 | 1 |
| 19 | Active Learning in Recurrent Neural Networks Facilitated by a Hebb-like Learning Rule with Memory | 2005 | 3 |
| 20 | A stochastic model for the estimation of perceptual switching events in pigeons | 2000 | 2 |
About Frank Emmert‐Streib
Frank Emmert‐Streib is a scholar working on Geometry and Topology, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 248 papers that have together received 5.4k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (79 papers), Gene expression and cancer classification (55 papers), Gene Regulatory Network Analysis (50 papers), Graph theory and applications (48 papers), Complex Network Analysis Techniques (47 papers), Computational Drug Discovery Methods (31 papers), Topological and Geometric Data Analysis (12 papers) and Topic Modeling (10 papers). The work is most often cited by research in Geometry and Topology (610 citations), Health Informatics (93 citations) and Computational Theory and Mathematics (1.1k citations). Frank Emmert‐Streib has collaborated with scholars based in Austria, Finland and United States. Frequent co-authors include Matthias Dehmer, Galina Glazko, Shailesh Tripathi, Gökmen Altay, Ricardo De Matos Simoes, Olli Yli‐Harja, Benjamin Haibe‐Kains, Yongtang Shi, Zhen Yang and Feng Han. Their work appears in journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.
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.