Utkarsh J. Dang

901 total citations
32 papers, 332 citations indexed

About

Utkarsh J. Dang is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Artificial Intelligence. According to data from OpenAlex, Utkarsh J. Dang has authored 32 papers receiving a total of 332 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 4 papers in Cardiology and Cardiovascular Medicine and 4 papers in Artificial Intelligence. Recurrent topics in Utkarsh J. Dang's work include Muscle Physiology and Disorders (14 papers), Bayesian Methods and Mixture Models (4 papers) and Cardiomyopathy and Myosin Studies (4 papers). Utkarsh J. Dang is often cited by papers focused on Muscle Physiology and Disorders (14 papers), Bayesian Methods and Mixture Models (4 papers) and Cardiomyopathy and Myosin Studies (4 papers). Utkarsh J. Dang collaborates with scholars based in United States, Canada and United Kingdom. Utkarsh J. Dang's co-authors include Paul D. McNicholas, Ryan P. Browne, Yetrib Hathout, Anthony J. Di Pasqua, Jeffrey M. Steele, Robert W. Seabury, Wesley D. Kufel, Bryan T. Mogle, Paula R. Clemens and Yi Shi and has published in prestigious journals such as Bioinformatics, PLoS ONE and The Journal of Clinical Endocrinology & Metabolism.

In The Last Decade

Utkarsh J. Dang

27 papers receiving 329 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Utkarsh J. Dang United States 11 180 43 42 34 33 32 332
Ziqing Yang China 12 255 1.4× 17 0.4× 41 1.0× 4 0.1× 17 0.5× 45 464
Katrina L. Easton United States 6 157 0.9× 33 0.8× 7 0.2× 7 0.2× 8 0.2× 9 360
Shujun Chen China 10 102 0.6× 33 0.8× 6 0.1× 68 2.0× 22 0.7× 21 352
Yuanyuan Duan China 11 85 0.5× 5 0.1× 27 0.6× 8 0.2× 10 0.3× 30 444
Songjie Chen United States 8 210 1.2× 7 0.2× 9 0.2× 7 0.2× 34 1.0× 12 393
Hannah Stower United States 9 189 1.1× 11 0.3× 44 1.0× 6 0.2× 27 0.8× 109 367
Joerg Schreiber Germany 10 219 1.2× 10 0.2× 19 0.5× 5 0.1× 17 0.5× 12 407
Yifan Jia China 11 84 0.5× 5 0.1× 25 0.6× 16 0.5× 55 1.7× 44 386
Haoyun Tang China 2 119 0.7× 4 0.1× 11 0.3× 8 0.2× 14 0.4× 4 306
Bin Feng China 10 115 0.6× 8 0.2× 18 0.4× 60 1.8× 30 0.9× 35 431

Countries citing papers authored by Utkarsh J. Dang

Since Specialization
Citations

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

Fields of papers citing papers by Utkarsh J. Dang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Utkarsh J. Dang. 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 Utkarsh J. Dang. The network helps show where Utkarsh J. Dang may publish in the future.

Co-authorship network of co-authors of Utkarsh J. Dang

This figure shows the co-authorship network connecting the top 25 collaborators of Utkarsh J. Dang. A scholar is included among the top collaborators of Utkarsh J. Dang 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 Utkarsh J. Dang. Utkarsh J. Dang 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.
Subedi, Sanjeena & Utkarsh J. Dang. (2025). Multivariate Poisson lognormal distribution for modeling counts from modern biological data: An overview. Computational and Structural Biotechnology Journal. 27. 1255–1264.
2.
Vera, Ana de, Paula R. Clemens, Utkarsh J. Dang, et al.. (2025). Mineralocorticoid receptor antagonism of vamorolone: Evidence from LIONHEART and VISION-DMD clinical trials. Steroids. 223. 109689–109689.
3.
Dang, Utkarsh J., Claire Wood, Sze Choong Wong, et al.. (2025). Height, weight, and body mass index trajectories and their correlation with functional outcome assessments in boys with Duchenne muscular dystrophy. Developmental Medicine & Child Neurology. 68(3). 429–440.
4.
Dang, Utkarsh J., et al.. (2025). Association of DMD Gene Variant Classes With Motor Outcomes in a Drug Registration Clinical Trial Setting. Neurology Genetics. 11(2). e200251–e200251. 1 indexed citations
5.
Ahmet, Alexandra, Utkarsh J. Dang, Raoul Rooman, et al.. (2024). Adrenal Suppression From Vamorolone and Prednisone in Duchenne Muscular Dystrophy: Results From the Phase 2b Clinical Trial. The Journal of Clinical Endocrinology & Metabolism. 110(2). 334–344. 3 indexed citations
6.
7.
Pegoraro, Elena, Luca Bello, Paula R. Clemens, et al.. (2024). Assessing Pharmacogenomic loci Associated with the Pharmacokinetics of Vamorolone in Boys with Duchenne Muscular Dystrophy. The Journal of Clinical Pharmacology. 64(9). 1130–1140. 1 indexed citations
8.
Dang, Utkarsh J., et al.. (2023). Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions. Journal of Classification. 40(1). 145–167. 5 indexed citations
9.
Dang, Utkarsh J., Kim M. Huffman, Tchilabalo Dilezitoko Alayi, et al.. (2023). A population-based study of children suggests blunted morning cortisol rhythms are associated with alterations of the systemic inflammatory state. Psychoneuroendocrinology. 159. 106411–106411. 2 indexed citations
10.
Clemens, Paula R., Heather Gordish‐Dressman, Gabriela Niizawa, et al.. (2023). Findings from the Longitudinal CINRG Becker Natural History Study. Journal of Neuromuscular Diseases. 11(1). 201–212. 2 indexed citations
11.
McDonald, Craig M., et al.. (2023). Modeling Early Heterogeneous Rates of Progression in Boys with Duchenne Muscular Dystrophy. Journal of Neuromuscular Diseases. 10(3). 349–364. 9 indexed citations
12.
Dang, Utkarsh J., et al.. (2022). Optimization of DOTAP/chol Cationic Lipid Nanoparticles for mRNA, pDNA, and Oligonucleotide Delivery. AAPS PharmSciTech. 23(5). 135–135. 38 indexed citations
13.
Soldin, Steven J., et al.. (2022). Acute serum protein and cytokine response of single dose of prednisone in adult volunteers. Steroids. 178. 108953–108953. 5 indexed citations
14.
Novak, James S., Utkarsh J. Dang, Alyson A. Fiorillo, et al.. (2021). Interrogation of Dystrophin and Dystroglycan Complex Protein Turnover After Exon Skipping Therapy. Journal of Neuromuscular Diseases. 8(s2). S383–S402. 15 indexed citations
15.
Uaesoontrachoon, Kitipong, Mamta Giri, Yetrib Hathout, et al.. (2021). Biomarker-focused multi-drug combination therapy and repurposing trial in mdx mice. PLoS ONE. 16(2). e0246507–e0246507. 11 indexed citations
16.
Hathout, Yetrib, Liang Chen, Michael Ogundele, et al.. (2019). Disease-specific and glucocorticoid-responsive serum biomarkers for Duchenne Muscular Dystrophy. Scientific Reports. 9(1). 12167–12167. 39 indexed citations
17.
Mogle, Bryan T., Jeffrey M. Steele, Robert W. Seabury, Utkarsh J. Dang, & Wesley D. Kufel. (2018). Implementation of a two-point pharmacokinetic AUC-based vancomycin therapeutic drug monitoring approach in patients with methicillin-resistant Staphylococcus aureus bacteraemia. International Journal of Antimicrobial Agents. 52(6). 805–810. 44 indexed citations
18.
Dang, Utkarsh J., Ryan P. Browne, & Paul D. McNicholas. (2015). Mixtures of Multivariate Power Exponential Distributions. Biometrics. 71(4). 1081–1089. 42 indexed citations
19.
20.
Dang, Utkarsh J. & Chris T. Bauch. (2010). A population biological approach to the collective dynamics of countries undergoing demographic transition. Journal of Theoretical Biology. 265(2). 167–176. 1 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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