K. Sujathan

818 total citations
49 papers, 633 citations indexed

About

K. Sujathan is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, K. Sujathan has authored 49 papers receiving a total of 633 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 11 papers in Computer Vision and Pattern Recognition and 10 papers in Artificial Intelligence. Recurrent topics in K. Sujathan's work include AI in cancer detection (10 papers), Glycosylation and Glycoproteins Research (8 papers) and Digital Imaging for Blood Diseases (7 papers). K. Sujathan is often cited by papers focused on AI in cancer detection (10 papers), Glycosylation and Glycoproteins Research (8 papers) and Digital Imaging for Blood Diseases (7 papers). K. Sujathan collaborates with scholars based in India, Sweden and United States. K. Sujathan's co-authors include P. Palanisamy, Swamidoss Issac Niwas, Varsha Karunakaran, Kaustabh Kumar Maiti, K. Raveendran Pillai, S. Kannan, Ewert Bengtsson, G. Saranya, Manu M. Joseph and Jyothi B. Nair and has published in prestigious journals such as Analytical Chemistry, Scientific Reports and ACS Applied Materials & Interfaces.

In The Last Decade

K. Sujathan

47 papers receiving 606 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
K. Sujathan India 15 240 123 109 95 83 49 633
Daniel O’Shea Ireland 10 152 0.6× 85 0.7× 74 0.7× 20 0.2× 69 0.8× 12 374
Alexandra Sala United Kingdom 10 188 0.8× 29 0.2× 82 0.8× 9 0.1× 201 2.4× 16 464
Liang Sang China 9 160 0.7× 21 0.2× 177 1.6× 13 0.1× 9 0.1× 38 500
Xianyang Luo China 12 224 0.9× 9 0.1× 59 0.5× 10 0.1× 113 1.4× 44 488
Edmond Kahn France 13 303 1.3× 41 0.3× 50 0.5× 29 0.3× 31 0.4× 29 773
Kadriye Çiftçi United States 9 242 1.0× 59 0.5× 60 0.6× 12 0.1× 24 0.3× 15 555
Yutian Wang China 19 541 2.3× 53 0.4× 59 0.5× 23 0.2× 11 0.1× 69 840
D Wittekind Germany 13 226 0.9× 69 0.6× 33 0.3× 42 0.4× 43 0.5× 74 573
Ning Tang China 12 323 1.3× 11 0.1× 136 1.2× 57 0.6× 7 0.1× 25 759
Hesham K. Yosef Germany 10 187 0.8× 17 0.1× 84 0.8× 9 0.1× 369 4.4× 16 523

Countries citing papers authored by K. Sujathan

Since Specialization
Citations

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

Fields of papers citing papers by K. Sujathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. Sujathan

This figure shows the co-authorship network connecting the top 25 collaborators of K. Sujathan. A scholar is included among the top collaborators of K. Sujathan 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 K. Sujathan. K. Sujathan 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.
Sujathan, K., et al.. (2024). esiRNA Mediated Silencing of HIF1A Regulates Migration, Invasion, Apoptosis, and Proliferation of MDA-MB-231 Cells. Cell and Tissue Biology. 18(5). 509–517. 1 indexed citations
2.
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Murali, Vishnu Priya, Varsha Karunakaran, Adukkadan N. Ramya, et al.. (2023). A clinically feasible diagnostic spectro-histology built on SERS-nanotags for multiplex detection and grading of breast cancer biomarkers. Biosensors and Bioelectronics. 227. 115177–115177. 30 indexed citations
4.
Guruvayoorappan, Chandrasekharan, et al.. (2023). Exploring the Phytochemical Profile and Biological Activities of Clerodendrum infortunatum. ACS Omega. 8(11). 10383–10396. 2 indexed citations
5.
Darvin, Pramod, K. M. Jagathnath Krishna, Paul Augustine, et al.. (2023). Co-expression of galectin-3 and vimentin in triple negative breast cancer cells promotes tumor progression, metastasis and survival. Tumor Biology. 45(1). 31–54. 7 indexed citations
6.
Thulaseedharan, Jissa Vinoda, et al.. (2023). 27‐Hydroxycholesterol represses G9a expression via oestrogen receptor alpha in breast cancer. Journal of Cellular and Molecular Medicine. 27(18). 2744–2755. 5 indexed citations
7.
George, Preethi Sara, et al.. (2021). Immunoexpression of TTF1 and p63 Differentiates Lung Adenocarcinomas in Sputum Samples. Journal of Cytology. 38(3). 151–157. 3 indexed citations
8.
Somanathan, Thara, et al.. (2021). Galectin-3: A factotum in carcinogenesis bestowing an archery for prevention. Tumor Biology. 43(1). 77–96. 10 indexed citations
9.
Karunakaran, Varsha, Manu M. Joseph, Jyothi B. Nair, et al.. (2020). Diagnostic spectro-cytology revealing differential recognition of cervical cancer lesions by label-free surface enhanced Raman fingerprints and chemometrics. Nanomedicine Nanotechnology Biology and Medicine. 29. 102276–102276. 37 indexed citations
10.
Sujathan, K., et al.. (2019). Immunocytochemistry on sputum samples predicts prognosis of lung cancer. Journal of Cytology. 36(1). 38–38. 3 indexed citations
11.
Sujathan, K., et al.. (2013). Antineoplastic effects of deoxyelephantopin, a sesquiterpene lactone from Elephantopus scaber, on lung adenocarcinoma (A549) cells. Journal of Integrative Medicine. 11(4). 269–277. 40 indexed citations
12.
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Sujathan, K., K Jayasree, & P Remani. (2009). Significance of a galactose specific plant lectin for the differential diagnosis of adenocarcinoma cells in effusion. Journal of Cytology. 26(4). 134–134. 1 indexed citations
14.
Pillai, K. Raveendran, et al.. (2009). Psammoma bodies in cervical smear in association with keratinizing squamous cell carcinoma of cervix: A case report. Diagnostic Cytopathology. 37(6). 450–454. 4 indexed citations
15.
Kannan, S., et al.. (1996). Expression of p53 in leukoplakia and squamous cell carcinoma of the oral mucosa: correlation with expression of Ki67. Molecular Pathology. 49(3). M170–M175. 39 indexed citations
16.
Pillai, K. Raveendran, P Remani, S. Kannan, et al.. (1996). Lectin histochemistry of oral premalignant and malignant lesions: Correlation of JFL and PNA binding pattern with tumour progression. European Journal of Cancer Part B Oral Oncology. 32(1). 32–37. 6 indexed citations
17.
Sujathan, K., et al.. (1996). Significance of AgNOR Count in Differentiating Malignant Cells from Reactive Mesothelial Cells in Serous Effusions. Acta Cytologica. 40(4). 724–728. 25 indexed citations
18.
Sujathan, K., K. Raveendran Pillai, N. Sreedevi Amma, et al.. (1996). Differential expression of jackfruit-lectin-specific glycoconjugates in metastatic adenocarcinoma and reactive mesothelial cells?a diagnostic aid in effusion cytology. Journal of Cancer Research and Clinical Oncology. 122(7). 433–436. 4 indexed citations
19.
20.
Pillai, K. Raveendran, P Remani, S. Kannan, et al.. (1994). Jack fruit lectin‐specific glycoconjugate expression during the progression of cervical intraepithelial neoplasia: A study on exfoliated cells. Diagnostic Cytopathology. 10(4). 342–346. 10 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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