James J. Cali

3.2k total citations
64 papers, 2.5k citations indexed

About

James J. Cali is a scholar working on Molecular Biology, Pharmacology and Computational Theory and Mathematics. According to data from OpenAlex, James J. Cali has authored 64 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 15 papers in Pharmacology and 13 papers in Computational Theory and Mathematics. Recurrent topics in James J. Cali's work include Pharmacogenetics and Drug Metabolism (15 papers), Computational Drug Discovery Methods (13 papers) and bioluminescence and chemiluminescence research (11 papers). James J. Cali is often cited by papers focused on Pharmacogenetics and Drug Metabolism (15 papers), Computational Drug Discovery Methods (13 papers) and bioluminescence and chemiluminescence research (11 papers). James J. Cali collaborates with scholars based in United States, France and Finland. James J. Cali's co-authors include David W. Russell, C L Hsieh, Uta Francke, Dermot M.F. Cooper, Jerzy Krupiński, N. Mons, John C. Zwaagstra, Dongping Ma, John Shultz and Poncho Meisenheimer and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Investigation and The Journal of Immunology.

In The Last Decade

James J. Cali

61 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James J. Cali United States 24 1.4k 608 483 402 326 64 2.5k
Kyle L. Kolaja United States 30 2.0k 1.4× 261 0.4× 381 0.8× 673 1.7× 308 0.9× 70 3.6k
Arco Y. Jeng United States 30 1.3k 0.9× 363 0.6× 480 1.0× 369 0.9× 127 0.4× 130 3.6k
Anca D. Petrescu United States 27 1.6k 1.1× 549 0.9× 394 0.8× 118 0.3× 83 0.3× 59 2.4k
Sean M. Armour United States 14 2.1k 1.5× 351 0.6× 268 0.6× 241 0.6× 45 0.1× 18 4.2k
Sabine Grösch Germany 42 2.8k 2.0× 361 0.6× 726 1.5× 278 0.7× 282 0.9× 84 5.2k
Masanori Yoshioka Japan 25 1.2k 0.8× 142 0.2× 280 0.6× 556 1.4× 114 0.3× 109 2.2k
Ilana Nissim United States 28 3.7k 2.6× 551 0.9× 454 0.9× 317 0.8× 37 0.1× 50 5.7k
Valerie G. Montana United States 17 3.5k 2.4× 350 0.6× 449 0.9× 621 1.5× 127 0.4× 18 4.9k

Countries citing papers authored by James J. Cali

Since Specialization
Citations

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

Fields of papers citing papers by James J. Cali

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James J. Cali

This figure shows the co-authorship network connecting the top 25 collaborators of James J. Cali. A scholar is included among the top collaborators of James J. Cali 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 James J. Cali. James J. Cali 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.
Sakamuru, Srilatha, Dongping Ma, Nancy Baker, et al.. (2024). Development and validation of CYP26A1 inhibition assay for high‐throughput screening. Biotechnology Journal. 19(6). e2300659–e2300659. 3 indexed citations
2.
Alves, Juliano, Marie K. Schwinn, Thomas Machleidt, et al.. (2024). Monitoring phosphorylation and acetylation of CRISPR-mediated HiBiT-tagged endogenous proteins. Scientific Reports. 14(1). 2138–2138. 2 indexed citations
3.
Teske, Kelly A., Cesear Corona, Jennifer Wilkinson, et al.. (2023). Interrogating direct NLRP3 engagement and functional inflammasome inhibition using cellular assays. Cell chemical biology. 31(2). 349–360.e6. 14 indexed citations
4.
Mondal, Subhanjan, et al.. (2021). A direct capture method for purification and detection of viral nucleic acid enables epidemiological surveillance of SARS-CoV-2. The Science of The Total Environment. 795. 148834–148834. 47 indexed citations
5.
6.
Valley, Michael P., Natasha Karassina, Natsuyo Aoyama, et al.. (2016). A bioluminescent assay for measuring glucose uptake. Analytical Biochemistry. 505. 43–50. 19 indexed citations
7.
Duellman, Sarah, Wenhui Zhou, Poncho Meisenheimer, et al.. (2015). Bioluminescent, Nonlytic, Real-Time Cell Viability Assay and Use in Inhibitor Screening. Assay and Drug Development Technologies. 13(8). 456–465. 52 indexed citations
8.
9.
Vidugirienė, Jolanta, Donna Leippe, Gediminas Vidugiris, et al.. (2014). Bioluminescent Cell-Based NAD(P)/NAD(P)H Assays for Rapid Dinucleotide Measurement and Inhibitor Screening. Assay and Drug Development Technologies. 12(9-10). 514–526. 27 indexed citations
10.
Auld, Douglas S., Henrike Veith, & James J. Cali. (2013). Bioluminescent Assays for Cytochrome P450 Enzymes. Methods in molecular biology. 987. 1–9. 15 indexed citations
11.
Swiss, Rachel, Andrew L. Niles, James J. Cali, Sashi Nadanaciva, & Yvonne Will. (2013). Validation of a HTS-amenable assay to detect drug-induced mitochondrial toxicity in the absence and presence of cell death. Toxicology in Vitro. 27(6). 1789–1797. 34 indexed citations
12.
Cali, James J., Dongping Ma, Monika G. Wood, Poncho Meisenheimer, & Dieter H. Klaubert. (2012). Bioluminescent assays for ADME evaluation: dialing in CYP selectivity with luminogenic substrates. Expert Opinion on Drug Metabolism & Toxicology. 8(9). 1115–1130. 33 indexed citations
13.
Banks, Peter, et al.. (2011). The Utility of Semi-Automating Multiplexed Assays for ADME/Tox Applications. Combinatorial Chemistry & High Throughput Screening. 14(8). 658–668. 1 indexed citations
14.
Garcia, Martha C., Dongping Ma, A. Thomas DiCioccio, & James J. Cali. (2008). The use of a high-throughput luminescent method to assess CYP3A enzyme induction in cultured rat hepatocytes. In Vitro Cellular & Developmental Biology - Animal. 44(5-6). 129–134. 6 indexed citations
15.
Valley, Michael P., Wenhui Zhou, John Shultz, et al.. (2006). A bioluminescent assay for monoamine oxidase activity. Analytical Biochemistry. 359(2). 238–246. 58 indexed citations
16.
Gu, Chen, James J. Cali, & Dermot M.F. Cooper. (2002). Dimerization of mammalian adenylate cyclases. European Journal of Biochemistry. 269(2). 413–421. 42 indexed citations
17.
Sussman, Norman, et al.. (2002). THE PREDICTIVE NATURE OF HIGH-THROUGHPUT TOXICITY SCREENING USING A HUMAN HEPATOCYTE CELL LINE. 12 indexed citations
18.
Krupiński, Jerzy & James J. Cali. (1997). 3 Molecular diversity of the adenylyl cyclases. PubMed. 32. 53–79. 23 indexed citations
19.
Leitersdorf, Eran, Ayeleth Reshef, Vardiella Meiner, et al.. (1993). Frameshift and splice-junction mutations in the sterol 27-hydroxylase gene cause cerebrotendinous xanthomatosis in Jews or Moroccan origin.. Journal of Clinical Investigation. 91(6). 2488–2496. 138 indexed citations
20.
Cohen, Jonathan C., James J. Cali, Diane F. Jelinek, et al.. (1992). Cloning of the human cholesterol 7α-hydroxylase gene (CYP7) and localization to chromosome 8q11–q12. Genomics. 14(1). 153–161. 87 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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