Alla Karnovsky

3.6k total citations
59 papers, 2.5k citations indexed

About

Alla Karnovsky is a scholar working on Molecular Biology, Physiology and Epidemiology. According to data from OpenAlex, Alla Karnovsky has authored 59 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Molecular Biology, 9 papers in Physiology and 6 papers in Epidemiology. Recurrent topics in Alla Karnovsky's work include Metabolomics and Mass Spectrometry Studies (33 papers), Bioinformatics and Genomic Networks (13 papers) and Diet and metabolism studies (6 papers). Alla Karnovsky is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (33 papers), Bioinformatics and Genomic Networks (13 papers) and Diet and metabolism studies (6 papers). Alla Karnovsky collaborates with scholars based in United States, Canada and China. Alla Karnovsky's co-authors include Kathleen A. Stringer, Charles Burant, Michael W. Klymkowsky, George Michailidis, Terry E. Weymouth, Gilbert S. Omenn, Charles R. Evans, H. V. Jagadish, Maureen A. Sartor and William L. Duren and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Environmental Science & Technology.

In The Last Decade

Alla Karnovsky

56 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alla Karnovsky United States 26 1.7k 357 280 201 190 59 2.5k
Gopal K. Marathe United States 32 1.5k 0.9× 410 1.1× 364 1.3× 160 0.8× 234 1.2× 69 3.3k
Olga Oskolkova Austria 28 1.8k 1.1× 266 0.7× 380 1.4× 202 1.0× 282 1.5× 59 3.0k
Noriko Iwamoto Japan 28 1.5k 0.9× 283 0.8× 139 0.5× 161 0.8× 136 0.7× 104 2.7k
Dmitry Grapov United States 29 1.1k 0.6× 188 0.5× 305 1.1× 178 0.9× 218 1.1× 44 2.0k
Paul C. Norris United States 30 1.2k 0.7× 382 1.1× 423 1.5× 245 1.2× 230 1.2× 46 3.6k
Sarantos Kostidis Netherlands 22 1.2k 0.7× 154 0.4× 290 1.0× 117 0.6× 192 1.0× 55 1.9k
Abdul R. Asif Germany 29 1.1k 0.6× 193 0.5× 288 1.0× 117 0.6× 101 0.5× 92 2.2k
Markus M. Rinschen Germany 28 1.8k 1.1× 167 0.5× 293 1.0× 350 1.7× 145 0.8× 83 2.9k
Jean-Paul Paı̈s de Barros France 34 1.7k 1.0× 421 1.2× 456 1.6× 118 0.6× 428 2.3× 123 3.7k
Sabine Weiskirchen Germany 24 816 0.5× 768 2.2× 251 0.9× 184 0.9× 152 0.8× 51 2.5k

Countries citing papers authored by Alla Karnovsky

Since Specialization
Citations

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

Fields of papers citing papers by Alla Karnovsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alla Karnovsky

This figure shows the co-authorship network connecting the top 25 collaborators of Alla Karnovsky. A scholar is included among the top collaborators of Alla Karnovsky 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 Alla Karnovsky. Alla Karnovsky 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.
Zhao, Yue, Masha G. Savelieff, Kai Guo, et al.. (2025). Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival. Nature Communications. 16(1). 9631–9631.
2.
Karnovsky, Alla, et al.. (2024). DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data. BMC Bioinformatics. 25(1). 383–383.
3.
Zhao, Yue, Stacey A. Sakowski, Lili Zhao, et al.. (2024). Epigenetic age acceleration is associated with occupational exposures, sex, and survival in amyotrophic lateral sclerosis. EBioMedicine. 109. 105383–105383. 6 indexed citations
4.
Rosario, Zaira, Carmen M. Vélez-Vega, Akram N. Alshawabkeh, et al.. (2024). Metabolomic Alterations Associated with Phthalate Exposures among Pregnant Women in Puerto Rico. Environmental Science & Technology. 58(41). 18076–18087.
5.
Shen, Tong, Oliver Fiehn, David A. Gaul, et al.. (2024). metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics. Metabolites. 14(2). 125–125. 2 indexed citations
6.
Harris, Sean M., et al.. (2022). Trichloroethylene modifies energy metabolites in the amniotic fluid of Wistar rats. Reproductive Toxicology. 109. 80–92. 2 indexed citations
7.
Ward, Kristen M., Kyle J. Burghardt, A. Zarina Kraal, et al.. (2021). Genetic and Metabolite Variability in One-Carbon Metabolism Applied to an Insulin Resistance Model in Patients With Schizophrenia Receiving Atypical Antipsychotics. Frontiers in Psychiatry. 12. 623143–623143. 2 indexed citations
9.
Bhatti, Umar F., Alla Karnovsky, Isabel S. Dennahy, et al.. (2020). Pharmacologic modulation of brain metabolism by valproic acid can induce a neuroprotective environment. The Journal of Trauma: Injury, Infection, and Critical Care. 90(3). 507–514. 5 indexed citations
10.
Evans, Charles R., Alla Karnovsky, Michael A. Puskarich, et al.. (2019). Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors. Journal of Proteome Research. 18(5). 2004–2011. 16 indexed citations
11.
Basu, Sumanta, William L. Duren, Charles R. Evans, et al.. (2017). Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics. 33(10). 1545–1553. 185 indexed citations
12.
Jin, Meiyan, Ting Han, Yao Yao, et al.. (2017). Glycolytic Enzymes Coalesce in G Bodies under Hypoxic Stress. Cell Reports. 20(4). 895–908. 143 indexed citations
13.
Stringer, Kathleen A., Ryan T. McKay, Alla Karnovsky, Bernadette Quémerais, & Paige Lacy. (2016). Metabolomics and Its Application to Acute Lung Diseases. Frontiers in Immunology. 7. 44–44. 99 indexed citations
14.
Stringer, Kathleen A., John G. Younger, Larisa Yeomans, et al.. (2015). Whole Blood Reveals More Metabolic Detail of the Human Metabolome than Serum as Measured by 1H-NMR Spectroscopy. Shock. 44(3). 200–208. 55 indexed citations
15.
Reka, Ajaya Kumar, Gang Chen, R.C. Jones, et al.. (2014). Epithelial-mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients. Carcinogenesis. 35(6). 1292–1300. 35 indexed citations
16.
Puskarich, Michael A., et al.. (2014). Pharmacometabolomics of l -Carnitine Treatment Response Phenotypes in Patients with Septic Shock. Annals of the American Thoracic Society. 12(1). 46–56. 49 indexed citations
17.
Lacy, Paige, Ryan T. McKay, Alla Karnovsky, et al.. (2014). Signal Intensities Derived from Different NMR Probes and Parameters Contribute to Variations in Quantification of Metabolites. PLoS ONE. 9(1). e85732–e85732. 36 indexed citations
18.
Kim, Jung H., Alla Karnovsky, Vasudeva Mahavisno, et al.. (2012). LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types. BMC Genomics. 13(1). 526–526. 60 indexed citations
19.
Karnovsky, Alla, Denise D. McKinley, Cara Ruble, et al.. (2003). A cluster of novel serotonin receptor 3-like genes on human chromosome 3. Gene. 319. 137–148. 69 indexed citations
20.
Klymkowsky, Michael W. & Alla Karnovsky. (1994). Morphogenesis and the Cytoskeleton: Studies of the Xenopus Embryo. Developmental Biology. 165(2). 372–384. 32 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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