Sarah Mubeen

579 total citations
19 papers, 277 citations indexed

About

Sarah Mubeen is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Sarah Mubeen has authored 19 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 2 papers in Artificial Intelligence. Recurrent topics in Sarah Mubeen's work include Bioinformatics and Genomic Networks (14 papers), Computational Drug Discovery Methods (5 papers) and Gene expression and cancer classification (5 papers). Sarah Mubeen is often cited by papers focused on Bioinformatics and Genomic Networks (14 papers), Computational Drug Discovery Methods (5 papers) and Gene expression and cancer classification (5 papers). Sarah Mubeen collaborates with scholars based in Germany, Spain and Italy. Sarah Mubeen's co-authors include Daniel Domingo‐Fernándéz, Martin Hofmann‐Apitius, Charles Tapley Hoyt, Alpha Tom Kodamullil, Holger Fröhlich, Yojana Gadiya, António J. Preto, Tamara Raschka, Shaurya Chanana and Matthew D. Healy and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and BMC Bioinformatics.

In The Last Decade

Sarah Mubeen

18 papers receiving 267 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sarah Mubeen Germany 9 193 71 20 19 19 19 277
Takeshi Hase Japan 10 289 1.5× 94 1.3× 25 1.3× 20 1.1× 45 2.4× 23 452
Elzbieta Rembeza Sweden 6 276 1.4× 31 0.4× 13 0.7× 8 0.4× 13 0.7× 7 354
Amitabha Chaudhuri United States 7 412 2.1× 102 1.4× 22 1.1× 13 0.7× 27 1.4× 9 480
Prashanth Athri India 10 259 1.3× 55 0.8× 14 0.7× 13 0.7× 33 1.7× 20 354
Nevila Hyka‐Nouspikel United Kingdom 11 421 2.2× 55 0.8× 10 0.5× 32 1.7× 16 0.8× 12 522
Sailendra Kumar Mahanta India 9 176 0.9× 25 0.4× 11 0.6× 9 0.5× 9 0.5× 10 315
Aleksandar Poleksić United States 9 245 1.3× 120 1.7× 36 1.8× 11 0.6× 6 0.3× 25 325
Samuel Sledzieski United States 7 233 1.2× 102 1.4× 12 0.6× 12 0.6× 13 0.7× 11 291
Anne Niknejad Switzerland 10 423 2.2× 37 0.5× 32 1.6× 20 1.1× 12 0.6× 17 484
Nathan Johnson United States 8 209 1.1× 87 1.2× 19 0.9× 40 2.1× 93 4.9× 15 433

Countries citing papers authored by Sarah Mubeen

Since Specialization
Citations

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

Fields of papers citing papers by Sarah Mubeen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Mubeen

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

All Works

19 of 19 papers shown
1.
Russo, Maria Francesca, Daniel Domingo‐Fernándéz, Andrea Zaliani, et al.. (2024). Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk. Data. 9(2). 25–25.
2.
Domingo‐Fernándéz, Daniel, et al.. (2024). Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process. Journal of Natural Products. 87(7). 1844–1851. 31 indexed citations
3.
Domingo‐Fernándéz, Daniel, et al.. (2023). Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions. Journal of Cheminformatics. 15(1). 107–107. 12 indexed citations
4.
Domingo‐Fernándéz, Daniel, et al.. (2023). Modern drug discovery using ethnobotany: A large-scale cross-cultural analysis of traditional medicine reveals common therapeutic uses. iScience. 26(9). 107729–107729. 15 indexed citations
5.
6.
Mubeen, Sarah, Alpha Tom Kodamullil, Martin Hofmann‐Apitius, & Daniel Domingo‐Fernándéz. (2022). On the influence of several factors on pathway enrichment analysis. Briefings in Bioinformatics. 23(3). 32 indexed citations
7.
Domingo‐Fernándéz, Daniel, et al.. (2022). Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery. PLoS Computational Biology. 18(2). e1009909–e1009909. 8 indexed citations
8.
Mubeen, Sarah, et al.. (2022). Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 122. 110688–110688. 5 indexed citations
9.
Mubeen, Sarah, et al.. (2022). Exploring the Complex Network of Heme-Triggered Effects on the Blood Coagulation System. Journal of Clinical Medicine. 11(19). 5975–5975. 3 indexed citations
10.
Mubeen, Sarah, et al.. (2021). DecoPath: a web application for decoding pathway enrichment analysis. NAR Genomics and Bioinformatics. 3(3). lqab087–lqab087. 3 indexed citations
11.
Birkenbihl, Colin, Sarah Mubeen, Jens Lehmann, et al.. (2021). CLEP: a hybrid data- and knowledge-driven framework for generating patient representations. Bioinformatics. 37(19). 3311–3318. 8 indexed citations
12.
13.
Domingo‐Fernándéz, Daniel, Sarah Mubeen, Charles Tapley Hoyt, et al.. (2021). A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer’s Disease. Journal of Alzheimer s Disease. 80(2). 831–840. 3 indexed citations
14.
Mubeen, Sarah, et al.. (2021). Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures. npj Systems Biology and Applications. 7(1). 40–40. 9 indexed citations
15.
Mubeen, Sarah, et al.. (2020). MultiPaths: a Python framework for analyzing multi-layer biological networks using diffusion algorithms. Bioinformatics. 37(1). 137–139. 3 indexed citations
16.
Mubeen, Sarah, et al.. (2020). Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current Opinion in Neurology. 33(2). 249–254. 13 indexed citations
17.
Mubeen, Sarah, et al.. (2020). Drug2ways: Reasoning over causal paths in biological networks for drug discovery. PLoS Computational Biology. 16(12). e1008464–e1008464. 18 indexed citations
18.
Mubeen, Sarah, et al.. (2019). The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Frontiers in Genetics. 10. 1203–1203. 71 indexed citations
19.
Domingo‐Fernándéz, Daniel, et al.. (2019). PathMe: merging and exploring mechanistic pathway knowledge. BMC Bioinformatics. 20(1). 243–243. 32 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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