Ritesh Krishna

517 total citations
27 papers, 363 citations indexed

About

Ritesh Krishna is a scholar working on Molecular Biology, Ecology and Spectroscopy. According to data from OpenAlex, Ritesh Krishna has authored 27 papers receiving a total of 363 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 7 papers in Ecology and 6 papers in Spectroscopy. Recurrent topics in Ritesh Krishna's work include Genomics and Phylogenetic Studies (8 papers), Machine Learning in Bioinformatics (6 papers) and Advanced Proteomics Techniques and Applications (6 papers). Ritesh Krishna is often cited by papers focused on Genomics and Phylogenetic Studies (8 papers), Machine Learning in Bioinformatics (6 papers) and Advanced Proteomics Techniques and Applications (6 papers). Ritesh Krishna collaborates with scholars based in United Kingdom, United States and Spain. Ritesh Krishna's co-authors include Andrew R. Jones, Fawaz Ghali, Jonathan M. Wastling, Dong Xia, Laura‐Jayne Gardiner, Henning Hermjakob, Florian Reisinger, Juan Antonio Vizcaíno, Stephen J. Cornell and Jenny A. Hodgson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Chemical Physics and PLoS ONE.

In The Last Decade

Ritesh Krishna

27 papers receiving 357 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ritesh Krishna United Kingdom 13 206 109 62 52 32 27 363
Kunal Aggarwal United States 6 333 1.6× 226 2.1× 26 0.4× 26 0.5× 10 0.3× 12 517
Gloria A. Murphy United States 11 192 0.9× 38 0.3× 35 0.6× 39 0.8× 23 0.7× 19 351
Xinzhe Yu United States 8 183 0.9× 14 0.1× 33 0.5× 14 0.3× 24 0.8× 10 343
Megan Choi United States 9 228 1.1× 153 1.4× 16 0.3× 9 0.2× 22 0.7× 12 368
Jun Mashima Japan 17 468 2.3× 17 0.2× 123 2.0× 60 1.2× 3 0.1× 24 625
Michael Powell United Kingdom 13 260 1.3× 10 0.1× 75 1.2× 42 0.8× 5 0.2× 17 585
M. Ehn Sweden 8 261 1.3× 6 0.1× 56 0.9× 22 0.4× 12 0.4× 9 375
Ngan Nguyen United States 7 729 3.5× 44 0.4× 35 0.6× 60 1.2× 11 0.3× 9 859
Luca Rappez United States 7 325 1.6× 118 1.1× 43 0.7× 38 0.7× 2 0.1× 7 494
Tom Naven United Kingdom 4 276 1.3× 174 1.6× 16 0.3× 32 0.6× 2 0.1× 4 389

Countries citing papers authored by Ritesh Krishna

Since Specialization
Citations

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

Fields of papers citing papers by Ritesh Krishna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ritesh Krishna

This figure shows the co-authorship network connecting the top 25 collaborators of Ritesh Krishna. A scholar is included among the top collaborators of Ritesh Krishna 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 Ritesh Krishna. Ritesh Krishna 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.
Gardiner, Laura‐Jayne, Claire Dearden, Anna Paola Carrieri, et al.. (2024). DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon. npj Biofilms and Microbiomes. 10(1). 113–113. 2 indexed citations
2.
3.
Gardiner, Laura‐Jayne, et al.. (2022). Scalable in-memory processing of omics workflows. Computational and Structural Biotechnology Journal. 20. 1914–1924. 1 indexed citations
4.
Neal, Andrew L., Xiaoxian Zhang, Taro Takahashi, et al.. (2022). Arable soil nitrogen dynamics reflect organic inputs via the extended composite phenotype. Nature Food. 4(1). 51–60. 12 indexed citations
5.
Gardiner, Laura‐Jayne, Rachel Rusholme‐Pilcher, Hannah Rees, et al.. (2021). Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function. Proceedings of the National Academy of Sciences. 118(32). 14 indexed citations
6.
Gardiner, Laura‐Jayne & Ritesh Krishna. (2021). Bluster or Lustre: Can AI Improve Crops and Plant Health?. Plants. 10(12). 2707–2707. 4 indexed citations
7.
Gardiner, Laura‐Jayne, Niina Haiminen, Filippo Utro, et al.. (2021). Re-purposing software for functional characterization of the microbiome. Microbiome. 9(1). 4–4. 8 indexed citations
8.
Goodhead, Ian, Frances Blow, Philip Brownridge, et al.. (2020). Large-scale and significant expression from pseudogenes in Sodalis glossinidius – a facultative bacterial endosymbiont. Microbial Genomics. 6(1). 13 indexed citations
9.
Utro, Filippo, Niina Haiminen, Enrico Siragusa, et al.. (2020). Hierarchically Labeled Database Indexing Allows Scalable Characterization of Microbiomes. iScience. 23(4). 100988–100988. 2 indexed citations
10.
Gardiner, Laura‐Jayne, et al.. (2020). Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity. Scientific Reports. 10(1). 9522–9522. 24 indexed citations
11.
Krishna, Ritesh, et al.. (2020). User-centric genomics infrastructure: trends and technologies. Genome. 64(4). 467–475. 2 indexed citations
12.
Turner, Joe, Ritesh Krishna, Arjèn E. van’t Hof, et al.. (2018). The sequence of a male-specific genome region containing the sex determination switch in Aedes aegypti. Parasites & Vectors. 11(1). 6 indexed citations
13.
Armstrong, Stuart D., Dong Xia, Ritesh Krishna, et al.. (2016). Stage-specific Proteomes from Onchocerca ochengi, Sister Species of the Human River Blindness Parasite, Uncover Adaptations to a Nodular Lifestyle. Molecular & Cellular Proteomics. 15(8). 2554–2575. 22 indexed citations
14.
Reisinger, Florian, Ritesh Krishna, Fawaz Ghali, et al.. (2012). jmzIdentML API: A Java interface to the mzIdentML standard for peptide and protein identification data. PROTEOMICS. 12(6). 790–794. 26 indexed citations
15.
Wastling, Jonathan M., Stuart D. Armstrong, Ritesh Krishna, & Dong Xia. (2012). Parasites, proteomes and systems: has Descartes’ clock run out of time?. Parasitology. 139(9). 1103–1118. 15 indexed citations
16.
Patterson, Elizabeth, R Webb, Allison B. Weisbrod, et al.. (2012). The microRNA expression changes associated with malignancy and SDHB mutation in pheochromocytoma. Endocrine Related Cancer. 19(2). 157–166. 50 indexed citations
17.
Wedge, David C., et al.. (2011). FDRAnalysis: A Tool for the Integrated Analysis of Tandem Mass Spectrometry Identification Results from Multiple Search Engines. Journal of Proteome Research. 10(4). 2088–2094. 15 indexed citations
18.
Krishna, Ritesh, Chang‐Tsun Li, & Vicky Buchanan‐Wollaston. (2010). A temporal precedence based clustering method for gene expression microarray data. BMC Bioinformatics. 11(1). 68–68. 8 indexed citations
19.
Krishna, Ritesh, et al.. (2010). A partial Granger causality based method for analysis of parameter interactions in bioreactors. Computers & Chemical Engineering. 35(1). 121–126. 5 indexed citations
20.
Feng, Jianfeng, Dongyun Yi, Ritesh Krishna, Shuixia Guo, & Vicky Buchanan‐Wollaston. (2009). Listen to Genes: Dealing with Microarray Data in the Frequency Domain. PLoS ONE. 4(4). e5098–e5098. 8 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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