Mugdha Khaladkar

1.1k total citations · 1 hit paper
16 papers, 591 citations indexed

About

Mugdha Khaladkar is a scholar working on Molecular Biology, Cancer Research and Biophysics. According to data from OpenAlex, Mugdha Khaladkar has authored 16 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 3 papers in Cancer Research and 2 papers in Biophysics. Recurrent topics in Mugdha Khaladkar's work include RNA Research and Splicing (10 papers), RNA modifications and cancer (8 papers) and RNA and protein synthesis mechanisms (6 papers). Mugdha Khaladkar is often cited by papers focused on RNA Research and Splicing (10 papers), RNA modifications and cancer (8 papers) and RNA and protein synthesis mechanisms (6 papers). Mugdha Khaladkar collaborates with scholars based in United States, Canada and United Kingdom. Mugdha Khaladkar's co-authors include Junhyong Kim, Peter T. Buckley, James Eberwine, Sridhar Hannenhalli, Bernhard Kühn, Joon Sang Lee, Melissa G. Mendez, Jack Pollard, Bartholomew J. Naughton and Ji Wen and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Nature Reviews Drug Discovery.

In The Last Decade

Mugdha Khaladkar

15 papers receiving 584 citations

Hit Papers

Applications of single-cell RNA sequencing in drug discov... 2023 2026 2024 2025 2023 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Applications of single-cell RNA sequencing in drug discovery and development
    2023 205 citations Bram Van de Sande, Joon Sang Lee et al. Nature Reviews Drug Discovery profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mugdha Khaladkar United States 12 457 95 59 55 40 16 591
Adam McDermaid United States 11 465 1.0× 87 0.9× 52 0.9× 105 1.9× 26 0.7× 16 655
Xinyuan Liu China 8 339 0.7× 61 0.6× 78 1.3× 32 0.6× 54 1.4× 13 503
Vipin Kumar United States 11 576 1.3× 91 1.0× 123 2.1× 80 1.5× 38 0.9× 30 801
Katy Vandereyken Belgium 3 457 1.0× 112 1.2× 81 1.4× 52 0.9× 66 1.6× 3 660
Ishaan Gupta India 17 988 2.2× 160 1.7× 77 1.3× 53 1.0× 51 1.3× 50 1.3k
Michael Pargett United States 15 482 1.1× 48 0.5× 68 1.2× 26 0.5× 66 1.6× 27 654
Soňa Legartová Czechia 18 653 1.4× 82 0.9× 22 0.4× 32 0.6× 100 2.5× 51 768
Shouguo Gao United States 16 444 1.0× 143 1.5× 155 2.6× 134 2.4× 66 1.6× 50 821
Oana Ursu United States 9 604 1.3× 66 0.7× 46 0.8× 85 1.5× 27 0.7× 13 696
Adam Faulconbridge United Kingdom 8 335 0.7× 42 0.4× 49 0.8× 62 1.1× 22 0.6× 12 564

Countries citing papers authored by Mugdha Khaladkar

Since Specialization
Citations

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

Fields of papers citing papers by Mugdha Khaladkar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mugdha Khaladkar

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

All Works

16 of 16 papers shown
1.
Sande, Bram Van de, Joon Sang Lee, Euphemia Mutasa-Gottgens, et al.. (2023). Applications of single-cell RNA sequencing in drug discovery and development. Nature Reviews Drug Discovery. 22(6). 496–520. 205 indexed citations breakdown →
2.
Han, Lu, Sangita Choudhury, Jocelyn D. Mich-Basso, et al.. (2020). Lamin B2 Levels Regulate Polyploidization of Cardiomyocyte Nuclei and Myocardial Regeneration. Developmental Cell. 53(1). 42–59.e11. 66 indexed citations
3.
Zhu, Qin, Stephen Fisher, Hannah Dueck, et al.. (2018). PIVOT: platform for interactive analysis and visualization of transcriptomics data. BMC Bioinformatics. 19(1). 6–6. 29 indexed citations
4.
Khaladkar, Mugdha, Gautier Koscielny, Samiul Hasan, et al.. (2017). Uncovering novel repositioning opportunities using the Open Targets platform. Drug Discovery Today. 22(12). 1800–1807. 12 indexed citations
5.
Dueck, Hannah, Mugdha Khaladkar, Tae Kyung Kim, et al.. (2015). Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation. Genome Biology. 16(1). 122–122. 66 indexed citations
6.
Khaladkar, Mugdha, Masashi Abe, Michael Parisi, et al.. (2015). The exonuclease Nibbler regulates age‐associated traits and modulates piRNA length in Drosophila. Aging Cell. 14(3). 443–452. 50 indexed citations
7.
Francis, Chantal, Miler T. Lee, Mugdha Khaladkar, et al.. (2014). Divergence of RNA localization between rat and mouse neurons reveals the potential for rapid brain evolution. BMC Genomics. 15(1). 883–883. 20 indexed citations
8.
Buckley, Peter T., Mugdha Khaladkar, Junhyong Kim, & James Eberwine. (2013). Cytoplasmic intron retention, function, splicing, and the sentinel RNA hypothesis. Wiley Interdisciplinary Reviews - RNA. 5(2). 223–230. 46 indexed citations
9.
Khaladkar, Mugdha, Peter T. Buckley, Miler T. Lee, et al.. (2013). Subcellular RNA Sequencing Reveals Broad Presence of Cytoplasmic Intron-Sequence Retaining Transcripts in Mouse and Rat Neurons. PLoS ONE. 8(10). e76194–e76194. 31 indexed citations
10.
Khaladkar, Mugdha & Sridhar Hannenhalli. (2012). Functional divergence of gene duplicates – a domain-centric view. BMC Evolutionary Biology. 12(1). 126–126. 18 indexed citations
11.
Khaladkar, Mugdha, et al.. (2011). Epigenomic and RNA structural correlates of polyadenylation. RNA Biology. 8(3). 529–537. 15 indexed citations
12.
Wang, Jason T. L., et al.. (2011). A METHOD FOR DISCOVERING COMMON PATTERNS FROM TWO RNA SECONDARY STRUCTURES AND ITS APPLICATION TO STRUCTURAL REPEAT DETECTION. Journal of Bioinformatics and Computational Biology. 10(4). 1250001–1250001. 2 indexed citations
13.
Khaladkar, Mugdha, et al.. (2008). Mining small RNA structure elements in untranslated regions of human and mouse mRNAs using structure-based alignment. BMC Genomics. 9(1). 189–189. 10 indexed citations
14.
Khaladkar, Mugdha, et al.. (2008). Detecting conserved secondary structures in RNA molecules using constrained structural alignment. Computational Biology and Chemistry. 32(4). 264–272. 4 indexed citations
15.
Khaladkar, Mugdha, et al.. (2007). RADAR: a web server for RNA data analysis and research. Nucleic Acids Research. 35(Web Server). W300–W304. 17 indexed citations
16.
Khaladkar, Mugdha, et al.. (2006). RADAR: An InteractiveWeb-Based Toolkit for RNA Data Analysis and Research. View. 6. 209–212.

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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