Sriram P. Chockalingam

506 total citations
22 papers, 308 citations indexed

About

Sriram P. Chockalingam is a scholar working on Molecular Biology, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Sriram P. Chockalingam has authored 22 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 11 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in Sriram P. Chockalingam's work include Genomics and Phylogenetic Studies (8 papers), Algorithms and Data Compression (7 papers) and Gene expression and cancer classification (6 papers). Sriram P. Chockalingam is often cited by papers focused on Genomics and Phylogenetic Studies (8 papers), Algorithms and Data Compression (7 papers) and Gene expression and cancer classification (6 papers). Sriram P. Chockalingam collaborates with scholars based in United States, India and China. Sriram P. Chockalingam's co-authors include Srinivas Aluru, Xiao Yang, Sharma V. Thankachan, Maneesha Aluru, Yongchao Liu, S. Geetha, N. Kamaraj, Alberto Apostolico, Sanchit Misra and Min Xie and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and BMC Bioinformatics.

In The Last Decade

Sriram P. Chockalingam

19 papers receiving 296 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sriram P. Chockalingam United States	9	207	88	52	32	28	22	308
Mourad Elloumi Tunisia	10	172 0.8×	85 1.0×	27 0.5×	26 0.8×	13 0.5×	50	351
Fatemeh Almodaresi United States	7	201 1.0×	69 0.8×	32 0.6×	26 0.8×	16 0.6×	11	246
Hasindu Gamaarachchi Australia	11	325 1.6×	97 1.1×	34 0.7×	41 1.3×	40 1.4×	37	538
Filippo Utro United States	11	348 1.7×	141 1.6×	46 0.9×	58 1.8×	37 1.3×	48	490
Raluca Uricaru France	6	217 1.0×	59 0.7×	30 0.6×	37 1.2×	25 0.9×	13	281
Phillip E. C. Compeau United States	6	325 1.6×	96 1.1×	93 1.8×	70 2.2×	36 1.3×	7	445
German Tischler United Kingdom	11	271 1.3×	79 0.9×	84 1.6×	96 3.0×	18 0.6×	21	486
Guillaume Holley Iceland	8	214 1.0×	59 0.7×	44 0.8×	59 1.8×	9 0.3×	12	266
Felipe Llinares-López Switzerland	11	174 0.8×	102 1.2×	36 0.7×	68 2.1×	18 0.6×	14	352
Alexandru I. Tomescu Finland	11	236 1.1×	147 1.7×	33 0.6×	44 1.4×	34 1.2×	68	437

Countries citing papers authored by Sriram P. Chockalingam

Since Specialization

Citations

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

Fields of papers citing papers by Sriram P. Chockalingam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sriram P. Chockalingam. 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 Sriram P. Chockalingam. The network helps show where Sriram P. Chockalingam may publish in the future.

Co-authorship network of co-authors of Sriram P. Chockalingam

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chockalingam, Sriram P., Maneesha Aluru, & Srinivas Aluru. (2025). SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data. Bioinformatics. 41(2).

Connolly, Erin C., Tony Pan, Maneesha Aluru, et al.. (2024). Loss of immune cell identity with age inferred from large atlases of single cell transcriptomes. Aging Cell. 23(12). e14306–e14306.

Pan, Tony, Sriram P. Chockalingam, Maneesha Aluru, & Srinivas Aluru. (2023). MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework. Bioinformatics. 39(6). 1 indexed citations

Chockalingam, Sriram P., et al.. (2023). A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery. IEEE Transactions on Parallel and Distributed Systems. 34(6). 1699–1715. 10 indexed citations

Aluru, Maneesha, et al.. (2021). EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks. Bioinformatics. 38(5). 1312–1319. 8 indexed citations

Chockalingam, Sriram P., et al.. (2020). A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery. 35. 1–15. 9 indexed citations

Chockalingam, Sriram P., et al.. (2020). An alignment-free heuristic for fast sequence comparisons with applications to phylogeny reconstruction. BMC Bioinformatics. 21(S6). 404–404.

Chockalingam, Sriram P., et al.. (2018). A Parallel Algorithm for Bayesian Network Inference Using Arithmetic Circuits. 34–43. 3 indexed citations

Thankachan, Sharma V., et al.. (2017). A greedy alignment-free distance estimator for phylogenetic inference. BMC Bioinformatics. 18(S8). 238–238. 20 indexed citations

10.

Chockalingam, Sriram P., Maneesha Aluru, Hongqing Guo, Yanhai Yin, & Srinivas Aluru. (2017). Reverse Engineering Gene Networks. 480–490. 2 indexed citations

11.

Chockalingam, Sriram P., Maneesha Aluru, & Srinivas Aluru. (2016). Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories. SHILAP Revista de lepidopterología. 5(3). 23–23. 11 indexed citations

12.

Chockalingam, Sriram P., Sharma V. Thankachan, & Srinivas Aluru. (2016). A Parallel Algorithm for Finding All Pairs κ-Mismatch Maximal Common Substrings. 784–794. 1 indexed citations

13.

Thankachan, Sharma V., Sriram P. Chockalingam, Yongchao Liu, Alberto Apostolico, & Srinivas Aluru. (2016). ALFRED: A Practical Method for Alignment-Free Distance Computation. Journal of Computational Biology. 23(6). 452–460. 18 indexed citations

14.

Campo, David S., et al.. (2015). Efficient detection of viral transmission with threshold-based methods. s10. 1–6. 2 indexed citations

15.

Thankachan, Sharma V., et al.. (2015). A greedy alignment-free distance estimator for phylogenetic inference (extended abstract). 1–1. 3 indexed citations

16.

Chockalingam, Sriram P., Maneesha Aluru, & Srinivas Aluru. (2015). Information Theory Based Genome-Scale Gene Networks Construction Using MapReduce. 5. 464–473. 1 indexed citations

17.

Chockalingam, Sriram P., et al.. (2015). Parallel Read Error Correction for Big Genomic Datasets. 446–455. 2 indexed citations

18.

Misra, Sanchit, et al.. (2014). Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks. 17. 461–472. 11 indexed citations

19.

Yang, Xiao, Sriram P. Chockalingam, & Srinivas Aluru. (2012). A survey of error-correction methods for next-generation sequencing. Briefings in Bioinformatics. 14(1). 56–66. 169 indexed citations

20.

Geetha, S., et al.. (2011). Varying radix numeral system based adaptive image steganography. Information Processing Letters. 111(16). 792–797. 20 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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