Frank Emmert‐Streib

8.5k total citations · 2 hit papers
248 papers, 5.4k citations indexed

About

Frank Emmert‐Streib is a scholar working on Molecular Biology, Computational Theory and Mathematics and Statistical and Nonlinear Physics. According to data from OpenAlex, Frank Emmert‐Streib has authored 248 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 109 papers in Molecular Biology, 61 papers in Computational Theory and Mathematics and 50 papers in Statistical and Nonlinear Physics. Recurrent topics in Frank Emmert‐Streib's work include Bioinformatics and Genomic Networks (79 papers), Gene expression and cancer classification (55 papers) and Gene Regulatory Network Analysis (50 papers). Frank Emmert‐Streib is often cited by papers focused on Bioinformatics and Genomic Networks (79 papers), Gene expression and cancer classification (55 papers) and Gene Regulatory Network Analysis (50 papers). Frank Emmert‐Streib collaborates with scholars based in Austria, Finland and United States. Frank Emmert‐Streib's 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 and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Frank Emmert‐Streib

234 papers receiving 5.3k citations

Hit Papers

align trajectories sort by overperformance

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.

An Introductory Review of Deep Learning for Prediction Models With Big Data

2020 380 citations Frank Emmert‐Streib, Shailesh Tripathi et al. profile →
Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges

2024 57 citations Olli Yli‐Harja, Frank Emmert‐Streib et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Frank Emmert‐Streib Austria	38	2.4k	1.1k	853	610	567	248	5.4k
Matthias Dehmer Austria	39	1.7k 0.7×	2.3k 2.2×	1.0k 1.2×	1.9k 3.1×	1.4k 2.5×	273	6.9k
Nataša Pržulj United Kingdom	37	4.4k 1.8×	1.5k 1.4×	669 0.8×	99 0.2×	1.0k 1.8×	92	6.0k
K.-I. Goh South Korea	31	3.6k 1.5×	1.4k 1.3×	384 0.5×	278 0.5×	3.5k 6.1×	74	8.2k
Jihong Guan China	34	2.2k 0.9×	986 0.9×	788 0.9×	199 0.3×	932 1.6×	292	4.5k
Shuigeng Zhou China	41	2.2k 0.9×	1.0k 1.0×	1.3k 1.6×	247 0.4×	1.1k 2.0×	360	6.2k
Mauricio Barahona United Kingdom	37	3.2k 1.3×	337 0.3×	478 0.6×	144 0.2×	1.8k 3.2×	169	7.8k
Xian Wu China	43	946 0.4×	1.1k 1.0×	1.8k 2.1×	189 0.3×	101 0.2×	453	7.7k
Erzsébet Ravasz Regan United States	22	2.7k 1.1×	475 0.4×	488 0.6×	168 0.3×	2.1k 3.7×	32	5.6k
Walter L. Ruzzo United States	43	5.0k 2.1×	880 0.8×	2.1k 2.5×	153 0.3×	207 0.4×	102	8.3k
Alexei Vázquez United States	47	4.8k 2.0×	532 0.5×	294 0.3×	204 0.3×	3.4k 6.0×	119	9.9k

Countries citing papers authored by Frank Emmert‐Streib

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Frank Emmert‐Streib

This figure shows the co-authorship network connecting the top 25 collaborators of Frank Emmert‐Streib. A scholar is included among the top collaborators of Frank Emmert‐Streib 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 Frank Emmert‐Streib. Frank Emmert‐Streib 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

Emmert‐Streib, Frank, et al.. (2025). The role of digital twins in P4 medicine: A paradigm for modern healthcare. npj Digital Medicine. 8(1). 735–735.

Yu, Guihai, et al.. (2024). Community detection in directed networks based on network embeddings. Chaos Solitons & Fractals. 189. 115630–115630. 2 indexed citations

Emmert‐Streib, Frank, et al.. (2024). Complexity data science: A spin-off from digital twins. PNAS Nexus. 3(11). pgae456–pgae456. 4 indexed citations

McInerney, Caitríona E., Kienan I. Savage, Stuart McIntosh, et al.. (2021). Robustness of differential gene expression analysis of RNA-seq. Computational and Structural Biotechnology Journal. 19. 3470–3481. 50 indexed citations

Holzinger, Andreas, Matthias Dehmer, Frank Emmert‐Streib, et al.. (2021). Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Information Fusion. 79. 263–278. 145 indexed citations

Mowshowitz, Abbe, Matthias Dehmer, & Frank Emmert‐Streib. (2019). A Note on Graphs with Prescribed Orbit Structure. Entropy. 21(11). 1118–1118. 2 indexed citations

Ghorbani, Modjtaba, Matthias Dehmer, Abbe Mowshowitz, Jin Tao, & Frank Emmert‐Streib. (2019). The Hosoya Entropy of Graphs Revisited. Symmetry. 11(8). 1013–1013. 15 indexed citations

Musa, Aliyu, Konda Mani Saravanan, Frank Emmert‐Streib, et al.. (2019). 2-(2-(2,4-dioxopentan-3-ylidene)hydrazineyl)benzonitrile as novel inhibitor of receptor tyrosine kinase and PI3K/AKT/mTOR signaling pathway in glioblastoma. European Journal of Medicinal Chemistry. 166. 291–303. 49 indexed citations

Ghorbani, Modjtaba, et al.. (2019). A Note on Distance-Based Entropy of Dendrimers. Axioms. 8(3). 98–98. 2 indexed citations

10.

Dehmer, Matthias, Zengqiang Chen, Frank Emmert‐Streib, et al.. (2019). Measuring the complexity of directed graphs: A polynomial-based approach. PLoS ONE. 14(11). e0223745–e0223745. 6 indexed citations

11.

Tripathi, Shailesh, Jason Lloyd‐Price, André S. Ribeiro, et al.. (2017). sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters. BMC Bioinformatics. 18(1). 325–325. 5 indexed citations

12.

Chen, Zengqiang, Matthias Dehmer, Frank Emmert‐Streib, Abbe Mowshowitz, & Yongtang Shi. (2017). Toward Measuring Network Aesthetics Based on Symmetry. Axioms. 6(2). 12–12. 5 indexed citations

13.

Emmert‐Streib, Frank & Matthias Dehmer. (2012). Exploring Statistical and Population Aspects of Network Complexity. PLoS ONE. 7(5). e34523–e34523. 29 indexed citations

14.

Emmert‐Streib, Frank, Galina Glazko, Gökmen Altay, & Ricardo De Matos Simoes. (2012). Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data. Frontiers in Genetics. 3. 8–8. 96 indexed citations

15.

Dehmer, Matthias, Alexander Mehler, & Frank Emmert‐Streib. (2007). Graph-theoretical Characterizations of Generalized Trees.. 113–117. 7 indexed citations

16.

Emmert‐Streib, Frank, Matthias Dehmer, & Chris Seidel. (2006). Influence of Prior Information on the Reconstruction of the Yeast Cell Cycle from Microarray Data.. 274(6). 477–482. 1 indexed citations

17.

Emmert‐Streib, Frank & Matthias Dehmer. (2006). Theoretical Bounds for the Number of Inferable Edges in Sparse Random Networks.. 472–476. 1 indexed citations

18.

Emmert‐Streib, Frank. (2005). Active Learning in Recurrent Neural Networks Facilitated by a Hebb-like Learning Rule with Memory. 3 indexed citations

19.

Emmert‐Streib, Frank, Matthias Dehmer, Jing Liu, & Max Mühlhäuser. (2005). A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer. TUbilio (Technical University of Darmstadt). 1(8). 495–500. 1 indexed citations

20.

Emmert‐Streib, Frank, Matthias Dehmer, Gökhan Bakır, & Max Mühlhäuser. (2005). Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series. TUbilio (Technical University of Darmstadt). 1(10). 575–579.

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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