Jayashankar Das

1.2k total citations
37 papers, 533 citations indexed

About

Jayashankar Das is a scholar working on Molecular Biology, Plant Science and Infectious Diseases. According to data from OpenAlex, Jayashankar Das has authored 37 papers receiving a total of 533 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 12 papers in Plant Science and 7 papers in Infectious Diseases. Recurrent topics in Jayashankar Das's work include Leptospirosis research and findings (7 papers), Viral Infections and Vectors (4 papers) and Antibiotic Resistance in Bacteria (4 papers). Jayashankar Das is often cited by papers focused on Leptospirosis research and findings (7 papers), Viral Infections and Vectors (4 papers) and Antibiotic Resistance in Bacteria (4 papers). Jayashankar Das collaborates with scholars based in India. Jayashankar Das's co-authors include A. A. Mao, Afzal Ansari, Pratap Jyoti Handique, Sushma Dave, Ashok Munjal, Rekha Khandia, Ruchi Tiwari, Shailja Singhal, Kuldeep Dhama and Raj Kumar Singh and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Jayashankar Das

36 papers receiving 518 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jayashankar Das India 15 285 138 87 55 53 37 533
Muhammad Waqas Pakistan 17 443 1.6× 58 0.4× 61 0.7× 55 1.0× 21 0.4× 36 767
Jianming Fan China 14 237 0.8× 63 0.5× 97 1.1× 14 0.3× 29 0.5× 34 561
Sufian M. ElAssouli Saudi Arabia 10 431 1.5× 82 0.6× 91 1.0× 10 0.2× 23 0.4× 25 723
Luís Gustavo Morello Brazil 13 212 0.7× 109 0.8× 100 1.1× 13 0.2× 12 0.2× 33 492
zhang zhang China 14 166 0.6× 132 1.0× 217 2.5× 36 0.7× 21 0.4× 162 756
Blanca Lilia Barrón Mexico 13 180 0.6× 45 0.3× 103 1.2× 20 0.4× 9 0.2× 29 521
Breno de Mello Silva Brazil 16 146 0.5× 91 0.7× 236 2.7× 27 0.5× 19 0.4× 48 670
Qiongqiong Zhou China 14 251 0.9× 179 1.3× 82 0.9× 7 0.1× 23 0.4× 36 608
Yingping Wang China 8 325 1.1× 62 0.4× 168 1.9× 22 0.4× 329 6.2× 17 744
Li Feng China 11 161 0.6× 29 0.2× 78 0.9× 11 0.2× 15 0.3× 33 455

Countries citing papers authored by Jayashankar Das

Since Specialization
Citations

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

Fields of papers citing papers by Jayashankar Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jayashankar Das

This figure shows the co-authorship network connecting the top 25 collaborators of Jayashankar Das. A scholar is included among the top collaborators of Jayashankar 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 Jayashankar Das. Jayashankar 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.
Mohanty, Jatindra Nath, Debasish Swapnesh Kumar Nayak, Sejal Shah, et al.. (2024). Genomic characterization and distribution of XDR Klebsiella pneumoniae ST15 carrying blaOXA-181 on ColKP3 plasmid from an ICH patient in eastern India: A global comparative analysis. Gene Reports. 36. 101978–101978. 2 indexed citations
2.
Mohanty, Jatindra Nath, et al.. (2024). Identification and Exploration of Transcripts Involved in Antibiotic Resistance Mechanism of Two Critical Superbugs. Gene Expression. 23(1). 1–14. 1 indexed citations
3.
Mohanty, Jatindra Nath, et al.. (2023). Colistin the last resort drug in 21st century antibiotics to combat Multidrug resistance superbugs. Journal of Experimental Biology and Agricultural Sciences. 11(6). 919–929. 3 indexed citations
4.
Dhanalakshmi, M., et al.. (2023). Mannose: a potential saccharide candidate in disease management. Medicinal Chemistry Research. 32(3). 391–408. 29 indexed citations
5.
Patel, Saumya, et al.. (2023). Deciphering the lysine acetylation pattern of leptospiral strains by in silico approach. Network Modeling Analysis in Health Informatics and Bioinformatics. 12(1). 1 indexed citations
6.
Shah, Sejal, M. Dhanalakshmi, Amisha Patel, et al.. (2022). Unravelling Vitamin B12 as a potential inhibitor against SARS-CoV-2: A computational approach. Informatics in Medicine Unlocked. 30. 100951–100951. 20 indexed citations
7.
Dhanalakshmi, M., et al.. (2022). Artificial Neural Network-Based Study Predicts GS-441524 as a Potential Inhibitor of SARS-CoV-2 Activator Protein Furin: a Polypharmacology Approach. Applied Biochemistry and Biotechnology. 194(10). 4511–4529. 8 indexed citations
8.
Patel, Saumya, et al.. (2021). A core and pan gene map of Leptospira genus and its interactions with human host. Microbial Pathogenesis. 162. 105347–105347. 9 indexed citations
9.
Mohanty, Jatindra Nath, et al.. (2020). De novo sequencing and transcriptome analysis of Indian Bael (Aegle marmelos L.). Gene Reports. 19. 100671–100671. 6 indexed citations
10.
Mohanty, Jatindra Nath, et al.. (2020). Documentation of conserved and novel miRNAs participated in plant secondary metabolic pathways of sanctified Aegle marmelos. Gene Reports. 21. 100943–100943. 2 indexed citations
11.
Munjal, Ashok, et al.. (2020). Mycobacterium lepromatosis genome exhibits unusually high CpG dinucleotide content and selection is key force in shaping codon usage. Infection Genetics and Evolution. 84. 104399–104399. 26 indexed citations
12.
13.
Sharma, Priyanka, et al.. (2019). De novo transcriptome of Gymnema sylvestre identified putative lncRNA and genes regulating terpenoid biosynthesis pathway. Scientific Reports. 9(1). 14876–14876. 14 indexed citations
14.
Das, Jayashankar, et al.. (2019). Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory. Scientific Reports. 9(1). 1434–1434. 22 indexed citations
15.
Khandia, Rekha, Shailja Singhal, Afzal Ansari, et al.. (2019). Analysis of Nipah Virus Codon Usage and Adaptation to Hosts. Frontiers in Microbiology. 10. 886–886. 111 indexed citations
16.
Ansari, Afzal, et al.. (2018). LeptoDB: an integrated database of genomics and proteomics resource ofLeptospira. Database. 2018. 5 indexed citations
17.
Das, Jayashankar, et al.. (2018). Exploring Leptospiral proteomes to identify potential candidates for vaccine design against Leptospirosis using an immunoinformatics approach. Scientific Reports. 8(1). 6935–6935. 34 indexed citations
18.
Kumar, Dinesh, et al.. (2017). Cross-Kingdom Regulation of Putative miRNAs Derived from Happy Tree in Cancer Pathway: A Systems Biology Approach. International Journal of Molecular Sciences. 18(6). 1191–1191. 41 indexed citations
19.
Das, Jayashankar, et al.. (2014). Genetic diversity and relationships of selected Polygonum species using RAPD analysis.. 4(2). 1–8. 1 indexed citations
20.
Das, Jayashankar, A. A. Mao, & Pratap Jyoti Handique. (2011). Volatile Constituents of Valeriana hardwickii Wall. Root Oil from Arunachal Pradesh, Eastern Himalaya. SHILAP Revista de lepidopterología. 9 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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