Kulandaivelu Mahalingam

470 total citations
11 papers, 301 citations indexed

About

Kulandaivelu Mahalingam is a scholar working on Molecular Biology, Surgery and Genetics. According to data from OpenAlex, Kulandaivelu Mahalingam has authored 11 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 3 papers in Surgery and 3 papers in Genetics. Recurrent topics in Kulandaivelu Mahalingam's work include RNA Research and Splicing (3 papers), Machine Learning in Bioinformatics (2 papers) and Genetic Associations and Epidemiology (2 papers). Kulandaivelu Mahalingam is often cited by papers focused on RNA Research and Splicing (3 papers), Machine Learning in Bioinformatics (2 papers) and Genetic Associations and Epidemiology (2 papers). Kulandaivelu Mahalingam collaborates with scholars based in India. Kulandaivelu Mahalingam's co-authors include Rajiv K. Singh, Vikas Patil, Kumaravel Somasundaram, Giridharan Periyasamy, Amit Khanna, Shruti Bhargava, Arimappamagan Arivazhagan, Vani Santosh and Alangar S. Hegde and has published in prestigious journals such as Gene, Oncotarget and PeerJ.

In The Last Decade

Kulandaivelu Mahalingam

11 papers receiving 293 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kulandaivelu Mahalingam India 8 150 77 60 55 34 11 301
Parween Abdulla Canada 5 114 0.8× 62 0.8× 37 0.6× 36 0.7× 35 1.0× 8 338
Milad Heidari Nia Iran 12 151 1.0× 89 1.2× 28 0.5× 67 1.2× 15 0.4× 29 360
Simon Hirschberger Germany 9 145 1.0× 125 1.6× 109 1.8× 20 0.4× 15 0.4× 14 317
Cécilia Légaré Canada 10 249 1.7× 69 0.9× 55 0.9× 38 0.7× 19 0.6× 21 381
Yiwen Zhou China 9 109 0.7× 63 0.8× 19 0.3× 19 0.3× 32 0.9× 21 274
Sheng-Hsiung Sheu Taiwan 8 96 0.6× 22 0.3× 69 1.1× 53 1.0× 38 1.1× 9 340
Yongzhao Zhang China 10 178 1.2× 46 0.6× 123 2.0× 35 0.6× 12 0.4× 12 401
Melisande Addison United Kingdom 9 97 0.6× 26 0.3× 69 1.1× 32 0.6× 37 1.1× 13 300
Kristina M. Stemler United States 7 87 0.6× 25 0.3× 103 1.7× 27 0.5× 18 0.5× 8 256
Xin-zheng Lu China 12 124 0.8× 21 0.3× 35 0.6× 40 0.7× 13 0.4× 31 320

Countries citing papers authored by Kulandaivelu Mahalingam

Since Specialization
Citations

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

Fields of papers citing papers by Kulandaivelu Mahalingam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kulandaivelu Mahalingam

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

All Works

11 of 11 papers shown
2.
Patil, Vikas & Kulandaivelu Mahalingam. (2018). Comprehensive analysis of Reverse Phase Protein Array data reveals characteristic unique proteomic signatures for glioblastoma subtypes. Gene. 685. 85–95. 9 indexed citations
3.
Patil, Vikas & Kulandaivelu Mahalingam. (2018). A four-protein expression prognostic signature predicts clinical outcome of lower-grade glioma. Gene. 679. 57–64. 14 indexed citations
4.
Patil, Vikas, Alangar S. Hegde, Arimappamagan Arivazhagan, et al.. (2018). Genetic landscape of long noncoding RNA (lncRNAs) in glioblastoma: identification of complex lncRNA regulatory networks and clinically relevant lncRNAs in glioblastoma. Oncotarget. 9(51). 29548–29564. 28 indexed citations
6.
Singh, Rajiv K., et al.. (2017). Molecular genetics of human obesity: A comprehensive review. Comptes Rendus Biologies. 340(2). 87–108. 148 indexed citations
7.
Singh, Rajiv K. & Kulandaivelu Mahalingam. (2016). In silico approach to identify non-synonymous SNPs in human obesity related gene, MC3R (melanocortin-3-receptor). Computational Biology and Chemistry. 67. 122–130. 12 indexed citations
8.
Singh, Rajiv K., et al.. (2016). IN SILICO ANALYSIS OF SNPs IN HUMAN OBESITY RELATED GENE, SLC6A14 (SOLUTE CARRIER FAMILY 6 (AMINO ACID TRANSPORTER), MEMBER 14). 4 indexed citations
9.
Bhargava, Shruti, Vikas Patil, Kulandaivelu Mahalingam, & Kumaravel Somasundaram. (2016). Elucidation of the genetic and epigenetic landscape alterations in RNA binding proteins in glioblastoma. Oncotarget. 8(10). 16650–16668. 28 indexed citations
10.
Singh, Rajiv K., et al.. (2015). IN SILICO ANALYSIS OF FAT MASS OBESITY ASSOCIATED (FTO) GENE USING COMPUTATIONAL ALGORITHMS. International Journal of Pharma and Bio Sciences. 5 indexed citations
11.
Khanna, Amit, et al.. (2011). Ets-1 expression and gemcitabine chemoresistance in pancreatic cancer cells. Cellular & Molecular Biology Letters. 16(1). 101–13. 34 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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