Sayoni Das

3.2k total citations
26 papers, 1.3k citations indexed

About

Sayoni Das is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cell Biology. According to data from OpenAlex, Sayoni Das has authored 26 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 3 papers in Cell Biology. Recurrent topics in Sayoni Das's work include Machine Learning in Bioinformatics (10 papers), Genomics and Phylogenetic Studies (10 papers) and Bioinformatics and Genomic Networks (8 papers). Sayoni Das is often cited by papers focused on Machine Learning in Bioinformatics (10 papers), Genomics and Phylogenetic Studies (10 papers) and Bioinformatics and Genomic Networks (8 papers). Sayoni Das collaborates with scholars based in United Kingdom, Ukraine and United States. Sayoni Das's co-authors include Christine Orengo, Natalie L. Dawson, Ian Sillitoe, Jonathan Lees, David Lee, Paul Ashford, Tony E. Lewis, Janet M. Thornton, Romain A. Studer and Sonja Lehtinen and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Sayoni Das

26 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sayoni Das United Kingdom 17 1.1k 244 102 89 74 26 1.3k
Tony E. Lewis United Kingdom 11 1.3k 1.2× 379 1.6× 131 1.3× 110 1.2× 69 0.9× 15 1.4k
Sebastian Bittrich United States 12 762 0.7× 162 0.7× 131 1.3× 79 0.9× 66 0.9× 23 1.1k
Yuan Feng United States 3 712 0.7× 146 0.6× 76 0.7× 87 1.0× 86 1.2× 4 984
Rajiv K. Kar India 23 800 0.7× 148 0.6× 80 0.8× 45 0.5× 116 1.6× 80 1.3k
Cristina Marino‐Buslje Argentina 22 901 0.8× 125 0.5× 99 1.0× 127 1.4× 181 2.4× 60 1.2k
Peter Haebel Germany 16 832 0.8× 236 1.0× 61 0.6× 164 1.8× 40 0.5× 24 1.1k
Human Rezaei France 23 1.5k 1.4× 149 0.6× 60 0.6× 45 0.5× 39 0.5× 62 1.8k
Nicola Bordin United Kingdom 16 642 0.6× 139 0.6× 70 0.7× 36 0.4× 79 1.1× 31 881
Jinrui Xu United States 6 652 0.6× 176 0.7× 86 0.8× 88 1.0× 90 1.2× 18 817
Leigh Willard Canada 4 654 0.6× 164 0.7× 87 0.9× 58 0.7× 72 1.0× 5 905

Countries citing papers authored by Sayoni Das

Since Specialization
Citations

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

Fields of papers citing papers by Sayoni Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sayoni Das

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

All Works

20 of 20 papers shown
1.
Chatzifrangkeskou, Maria, Tess A. Stanly, Luana Campos Soares, et al.. (2025). ATR-hippo drives force signaling to nuclear F-actin and links mechanotransduction to neurological disorders. Science Advances. 11(7). eadr5683–eadr5683. 3 indexed citations
2.
Das, Sayoni, et al.. (2025). Actively protective combinatorial analysis: A scalable novel method for detecting variants that contribute to reduced disease prevalence in high-risk individuals. SHILAP Revista de lepidopterología. 7. 100125–100125. 1 indexed citations
3.
Pearson, Matthew, et al.. (2025). Reproducibility of genetic risk factors identified for long COVID using combinatorial analysis across US and UK patient cohorts with diverse ancestries. Journal of Translational Medicine. 23(1). 516–516. 1 indexed citations
4.
Pearson, Matthew, et al.. (2023). Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis. Journal of Translational Medicine. 21(1). 775–775. 25 indexed citations
5.
Tossounian, Maria‐Armineh, Sayoni Das, Darío A. Estrı́n, et al.. (2022). Profiling the Site of Protein CoAlation and Coenzyme A Stabilization Interactions. Antioxidants. 11(7). 1362–1362. 14 indexed citations
6.
Das, Sayoni, et al.. (2022). Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics. Patterns. 3(6). 100496–100496. 3 indexed citations
7.
Das, Sayoni, et al.. (2022). Genetic risk factors for ME/CFS identified using combinatorial analysis. Journal of Translational Medicine. 20(1). 598–598. 22 indexed citations
8.
Das, Sayoni, et al.. (2021). Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients. Frontiers in Digital Health. 3. 660809–660809. 4 indexed citations
9.
Das, Sayoni, Harry Scholes, Neeladri Sen, & Christine Orengo. (2020). CATH functional families predict functional sites in proteins. Bioinformatics. 37(8). 1099–1106. 19 indexed citations
10.
Sillitoe, Ian, Natalie L. Dawson, Tony E. Lewis, et al.. (2018). CATH: expanding the horizons of structure-based functional annotations for genome sequences. Nucleic Acids Research. 47(D1). D280–D284. 93 indexed citations
11.
Lam, Su Datt, Sayoni Das, Ian Sillitoe, & Christine Orengo. (2017). An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences. Acta Crystallographica Section D Structural Biology. 73(8). 628–640. 36 indexed citations
12.
Das, Sayoni, et al.. (2016). Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases. PLoS Computational Biology. 12(6). e1004926–e1004926. 19 indexed citations
13.
Dawson, Natalie L., Tony E. Lewis, Sayoni Das, et al.. (2016). CATH: an expanded resource to predict protein function through structure and sequence. Nucleic Acids Research. 45(D1). D289–D295. 253 indexed citations
14.
Lam, Su Datt, Natalie L. Dawson, Sayoni Das, et al.. (2015). Gene3D: expanding the utility of domain assignments. Nucleic Acids Research. 44(D1). D404–D409. 45 indexed citations
15.
Das, Sayoni, Ian Sillitoe, Jonathan Lees, et al.. (2015). CATH FunFHMMer web server: protein functional annotations using functional family assignments. Nucleic Acids Research. 43(W1). W148–W153. 54 indexed citations
16.
Das, Sayoni, Natalie L. Dawson, & Christine Orengo. (2015). Diversity in protein domain superfamilies. Current Opinion in Genetics & Development. 35. 40–49. 32 indexed citations
17.
Sillitoe, Ian, Tony E. Lewis, Alison Cuff, et al.. (2014). CATH: comprehensive structural and functional annotations for genome sequences. Nucleic Acids Research. 43(D1). D376–D381. 328 indexed citations
18.
Dawson, Natalie L., Romain A. Studer, Nicholas Furnham, et al.. (2014). WHAT CAN COMPARATIVE GENOMICS REVEAL ABOUT THE MECHANISMS OF PROTEIN FUNCTION EVOLUTION?. 16–19. 1 indexed citations
19.
Lees, Jonathan, Romain A. Studer, Natalie L. Dawson, et al.. (2013). Gene3D: Multi-domain annotations for protein sequence and comparative genome analysis. Nucleic Acids Research. 42(D1). D240–D245. 43 indexed citations
20.
Das, Sayoni, et al.. (2011). The chemical formula of a magnetotactic bacterium. Biotechnology and Bioengineering. 109(5). 1205–1216. 28 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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