Surojit Biswas

4.0k total citations · 4 hit papers
15 papers, 2.4k citations indexed

About

Surojit Biswas is a scholar working on Molecular Biology, Plant Science and Biotechnology. According to data from OpenAlex, Surojit Biswas has authored 15 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Plant Science and 2 papers in Biotechnology. Recurrent topics in Surojit Biswas's work include RNA and protein synthesis mechanisms (4 papers), Protein Structure and Dynamics (3 papers) and Plant-Microbe Interactions and Immunity (3 papers). Surojit Biswas is often cited by papers focused on RNA and protein synthesis mechanisms (4 papers), Protein Structure and Dynamics (3 papers) and Plant-Microbe Interactions and Immunity (3 papers). Surojit Biswas collaborates with scholars based in United States, United Kingdom and India. Surojit Biswas's co-authors include George M. Church, Ethan C. Alley, Grigory Khimulya, Mohammed AlQuraishi, Philip A. Wigge, Sandra Cortijo, Varodom Charoensawan, Katja E. Jaeger, Mathew S. Box and Daphne Ezer and has published in prestigious journals such as Science, Nature Communications and Nature Biotechnology.

In The Last Decade

Surojit Biswas

14 papers receiving 2.4k citations

Hit Papers

Phytochromes function as thermosensors in Arabidopsis 2016 2026 2019 2022 2016 2019 2022 2021 200 400 600
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.

  1. Phytochromes function as thermosensors in Arabidopsis
    2016 703 citations Jae‐Hoon Jung, Mirela Domijan et al. Science profile →
  2. Unified rational protein engineering with sequence-based deep representation learning
    2019 659 citations Ethan C. Alley, Grigory Khimulya et al. Nature Methods profile →
  3. Single-sequence protein structure prediction using a language model and deep learning
    2022 234 citations Ratul Chowdhury, Nazim Bouatta et al. Nature Biotechnology profile →
  4. Low-N protein engineering with data-efficient deep learning
    2021 228 citations Surojit Biswas, Grigory Khimulya et al. Nature Methods profile →

Peers

Surojit Biswas
Emmanuel Boutet Switzerland
A. A. Schaffer United States
Castrense Savojardo Italy
Damien Lieberherr Switzerland
Séverine Duvaud Switzerland
Taner Z. Sen United States
Toshiaki Katayama Japan
Alfonso Jaramillo France
Ainslie B. Parsons Canada
Sylvain Poux Switzerland
Emmanuel Boutet Switzerland
Surojit Biswas
Citations per year, relative to Surojit Biswas Surojit Biswas (= 1×) peers Emmanuel Boutet

Countries citing papers authored by Surojit Biswas

Since Specialization
Citations

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

Fields of papers citing papers by Surojit Biswas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Surojit Biswas

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

All Works

15 of 15 papers shown
1.
Chowdhury, Ratul, Nazim Bouatta, Surojit Biswas, et al.. (2022). Single-sequence protein structure prediction using a language model and deep learning. Nature Biotechnology. 40(11). 1617–1623. 234 indexed citations breakdown →
2.
Biswas, Surojit, Grigory Khimulya, Ethan C. Alley, Kevin M. Esvelt, & George M. Church. (2021). Low-N protein engineering with data-efficient deep learning. Nature Methods. 18(4). 389–396. 228 indexed citations breakdown →
3.
Alley, Ethan C., Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, & George M. Church. (2019). Unified rational protein engineering with sequence-based deep representation learning. Nature Methods. 16(12). 1315–1322. 659 indexed citations breakdown →
4.
Biswas, Surojit, et al.. (2018). Compare the effectiveness of static stretching and muscle energy technique on hamstring tightness among student population. 3(2). 140–143. 2 indexed citations
5.
Ezer, Daphne, Jae‐Hoon Jung, Hui Y. Lan, et al.. (2017). The evening complex coordinates environmental and endogenous signals in Arabidopsis. Nature Plants. 3(7). 17087–17087. 215 indexed citations
6.
Ezer, Daphne, Anna Brestovitsky, Patrick Dickinson, et al.. (2017). The G-Box Transcriptional Regulatory Code in Arabidopsis. PLANT PHYSIOLOGY. 175(2). 628–640. 110 indexed citations
7.
Yang, Li, Paulo José Pereira Lima Teixeira, Surojit Biswas, et al.. (2017). Pseudomonas syringae Type III Effector HopBB1 Promotes Host Transcriptional Repressor Degradation to Regulate Phytohormone Responses and Virulence. Cell Host & Microbe. 21(2). 156–168. 108 indexed citations
8.
Biswas, Surojit, et al.. (2017). Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes. Nature Communications. 8(1). 15309–15309. 10 indexed citations
9.
Jung, Jae‐Hoon, Mirela Domijan, Cornelia Klose, et al.. (2016). Phytochromes function as thermosensors in Arabidopsis. Science. 354(6314). 886–889. 703 indexed citations breakdown →
10.
Biswas, Surojit, Meredith McDonald, Derek S. Lundberg, Jeffery L. Dangl, & Vladimir Jojic. (2016). Learning Microbial Interaction Networks from Metagenomic Count Data. Journal of Computational Biology. 23(6). 526–535. 39 indexed citations
11.
Mucyn, Tatiana S., Scott Yourstone, Abigail Lind, et al.. (2014). Variable Suites of Non-effector Genes Are Co-regulated in the Type III Secretion Virulence Regulon across the Pseudomonas syringae Phylogeny. PLoS Pathogens. 10(1). e1003807–e1003807. 24 indexed citations
12.
Baltrus, David A., Marc T. Nishimura, Kevin Dougherty, et al.. (2012). The Molecular Basis of Host Specialization in Bean Pathovars of Pseudomonas syringae. Molecular Plant-Microbe Interactions. 25(7). 877–888. 45 indexed citations
13.
Mondal, Tapan Kumar, et al.. (2002). Superantigen-Induced Apoptotic Death of Tumor Cells Is Mediated by Cytotoxic Lymphocytes, Cytokines, and Nitric Oxide. Biochemical and Biophysical Research Communications. 290(4). 1336–1342. 24 indexed citations
14.
Mondal, Tapan Kumar, et al.. (2002). REPEATED TREATMENT WITHS. AUREUSSUPERANTIGENS EXPANDS THE SURVIVAL RATE OF EHRLICH ASCITES TUMOR BEARING MICE. Immunological Investigations. 31(1). 13–28. 1 indexed citations
15.
Biswas, Surojit. (2000). Computational and simulation studies of jute fibre length distribution.

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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