Subina Mehta

2.7k total citations
26 papers, 246 citations indexed

About

Subina Mehta is a scholar working on Molecular Biology, Spectroscopy and Infectious Diseases. According to data from OpenAlex, Subina Mehta has authored 26 papers receiving a total of 246 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 15 papers in Spectroscopy and 3 papers in Infectious Diseases. Recurrent topics in Subina Mehta's work include Advanced Proteomics Techniques and Applications (15 papers), Metabolomics and Mass Spectrometry Studies (7 papers) and Genomics and Phylogenetic Studies (7 papers). Subina Mehta is often cited by papers focused on Advanced Proteomics Techniques and Applications (15 papers), Metabolomics and Mass Spectrometry Studies (7 papers) and Genomics and Phylogenetic Studies (7 papers). Subina Mehta collaborates with scholars based in United States, Germany and Belgium. Subina Mehta's co-authors include Pratik Jagtap, Timothy J. Griffin, James E. Johnson, Praveen Kumar, Thomas McGowan, Bjoern Gruening, Joel Rudney, Bart Mesuere, Brook L. Nunn and Shane L. Hubler and has published in prestigious journals such as PLoS ONE, Scientific Reports and Molecular & Cellular Proteomics.

In The Last Decade

Subina Mehta

26 papers receiving 244 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Subina Mehta United States 10 186 118 26 18 17 26 246
Karen Culotta France 7 137 0.7× 51 0.4× 38 1.5× 51 2.8× 19 1.1× 8 287
Pieter Verschaffelt Belgium 7 148 0.8× 77 0.7× 26 1.0× 7 0.4× 3 0.2× 12 191
Henning Schiebenhoefer Germany 5 120 0.6× 57 0.5× 27 1.0× 12 0.7× 4 0.2× 5 156
Ming Wen China 6 204 1.1× 90 0.8× 26 1.0× 25 1.4× 5 0.3× 11 307
Markus Pioch Germany 7 131 0.7× 123 1.0× 13 0.5× 6 0.3× 3 0.2× 7 221
Joel A. Kooren United States 5 230 1.2× 156 1.3× 43 1.7× 3 0.2× 5 0.3× 5 355
Kevin L. Crowell United States 8 184 1.0× 189 1.6× 19 0.7× 5 0.3× 2 0.1× 8 270
Bon Ikwuagwu United States 4 104 0.6× 105 0.9× 34 1.3× 17 0.9× 2 0.1× 5 208
Garshasb Rigi Iran 11 148 0.8× 8 0.1× 15 0.6× 30 1.7× 7 0.4× 26 245
Joshua Hansen United States 6 101 0.5× 34 0.3× 9 0.3× 12 0.7× 14 151

Countries citing papers authored by Subina Mehta

Since Specialization
Citations

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

Fields of papers citing papers by Subina Mehta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Subina Mehta

This figure shows the co-authorship network connecting the top 25 collaborators of Subina Mehta. A scholar is included among the top collaborators of Subina Mehta 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 Subina Mehta. Subina Mehta 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.
Ryu, Joohyun, Mihir Shetty, Subina Mehta, et al.. (2025). Investigating proteogenomic divergence in patient-derived xenograft models of ovarian cancer. Scientific Reports. 15(1). 813–813. 2 indexed citations
2.
Mehta, Subina, et al.. (2024). A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. mSphere. 9(6). e0079323–e0079323. 3 indexed citations
3.
Mehta, Subina, et al.. (2024). Metaproteomics for Coinfections in the Upper Respiratory Tract: The Case of COVID-19. Methods in molecular biology. 2820. 165–185. 1 indexed citations
4.
Issac, Praveen Kumar, James E. Johnson, Thomas McGowan, et al.. (2024). Discovering Novel Proteoforms Using Proteogenomic Workflows Within the Galaxy Bioinformatics Platform. Methods in molecular biology. 2859. 109–128. 1 indexed citations
5.
Delogu, Francesco, Praveen Kumar, Benoît J. Kunath, et al.. (2023). Integrative meta-omics in Galaxy and beyond. Environmental Microbiome. 18(1). 56–56. 9 indexed citations
6.
Higgins, LeeAnn, Todd W. Markowski, Pratik Jagtap, et al.. (2023). An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples. Clinical Proteomics. 20(1). 14–14. 6 indexed citations
7.
Mehta, Subina, et al.. (2023). Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients. Journal of Proteome Research. 22(8). 2608–2619. 7 indexed citations
8.
Mehta, Subina, Valdemir Melechco Carvalho, Olivier Pible, et al.. (2022). Catching the Wave: Detecting Strain-Specific SARS-CoV-2 Peptides in Clinical Samples Collected during Infection Waves from Diverse Geographical Locations. Viruses. 14(10). 2205–2205. 1 indexed citations
9.
Mehta, Subina, Praveen Kumar, James E. Johnson, et al.. (2021). Updates on metaQuantome Software for Quantitative Metaproteomics. Journal of Proteome Research. 20(4). 2130–2137. 5 indexed citations
10.
Mehta, Subina, Björn Grüning, James E. Johnson, et al.. (2021). A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19). Clinical Proteomics. 18(1). 15–15. 8 indexed citations
11.
Thuy-Boun, Peter, Andrew McArdle, Subina Mehta, et al.. (2021). ComPIL 2.0 and MetaNovo tools for metaproteomics searches within Galaxy: searching for needles in a Haystack. FreiDok plus (Universitätsbibliothek Freiburg). 10. 1 indexed citations
12.
McGowan, Thomas, James E. Johnson, Praveen Kumar, et al.. (2020). Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration. GigaScience. 9(4). 13 indexed citations
13.
Kumar, Praveen, James E. Johnson, Subina Mehta, et al.. (2020). A Sectioning and Database Enrichment Approach for Improved Peptide Spectrum Matching in Large, Genome-Guided Protein Sequence Databases. Journal of Proteome Research. 19(7). 2772–2785. 23 indexed citations
14.
Mehta, Subina, Robert J. Millikin, Ignacio Eguinoa, et al.. (2020). Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform. Proteomes. 8(3). 15–15. 10 indexed citations
15.
Riffle, Michael, Bart Mesuere, Thilo Muth, et al.. (2020). Survey of metaproteomics software tools for functional microbiome analysis. PLoS ONE. 15(11). e0241503–e0241503. 30 indexed citations
16.
Hubler, Shane L., Praveen Kumar, Subina Mehta, et al.. (2019). Challenges in Peptide-Spectrum Matching: A Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits. Journal of Proteome Research. 19(1). 161–173. 11 indexed citations
17.
Stewart, Paul A., Brent M. Kuenzi, Subina Mehta, et al.. (2019). The Galaxy Platform for Reproducible Affinity Proteomic Mass Spectrometry Data Analysis. Methods in molecular biology. 1977. 249–261. 5 indexed citations
18.
Mehta, Subina, James E. Johnson, Praveen Kumar, et al.. (2019). metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes. Molecular & Cellular Proteomics. 18(8). S82–S91. 25 indexed citations
19.
Staes, An, Björn Grüning, Subina Mehta, et al.. (2018). Update on the moFF Algorithm for Label-Free Quantitative Proteomics. Journal of Proteome Research. 18(2). 728–731. 9 indexed citations
20.

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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