Igor F. Tsigelny

7.5k total citations · 1 hit paper
166 papers, 5.4k citations indexed

About

Igor F. Tsigelny is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Igor F. Tsigelny has authored 166 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 92 papers in Molecular Biology, 33 papers in Computational Theory and Mathematics and 25 papers in Cancer Research. Recurrent topics in Igor F. Tsigelny's work include Computational Drug Discovery Methods (33 papers), Cholinesterase and Neurodegenerative Diseases (18 papers) and Alzheimer's disease research and treatments (12 papers). Igor F. Tsigelny is often cited by papers focused on Computational Drug Discovery Methods (33 papers), Cholinesterase and Neurodegenerative Diseases (18 papers) and Alzheimer's disease research and treatments (12 papers). Igor F. Tsigelny collaborates with scholars based in United States, France and Brazil. Igor F. Tsigelny's co-authors include Valentina L. Kouznetsova, Palmer Taylor, Eliezer Masliah, Susan S. Taylor, Lynn F. Ten Eyck, Tuula Kallunki, Graham Moore, Roger J. Davis, Hayla K. Sluss and Bing Su and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Neuroscience.

In The Last Decade

Igor F. Tsigelny

156 papers receiving 5.3k citations

Hit Papers

JNK2 contains a specificity-determining region responsibl... 1994 2026 2004 2015 1994 100 200 300 400 500
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. JNK2 contains a specificity-determining region responsible for efficient c-Jun binding and phosphorylation.
    1994 561 citations Igor F. Tsigelny et al. profile →

Peers

Igor F. Tsigelny
Lawrence C. Fritz United States
Jing Huang United States
Andrew K. Shiau United States
Hengming Ke United States
Jenny Bain United Kingdom
Irina Kufareva United States
George P. Livi United States
Marie A. Bogoyevitch Australia
François M. Vallette France
Takatsugu Hirokawa Japan
Lawrence C. Fritz United States
Igor F. Tsigelny
Citations per year, relative to Igor F. Tsigelny Igor F. Tsigelny (= 1×) peers Lawrence C. Fritz

Countries citing papers authored by Igor F. Tsigelny

Since Specialization
Citations

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

Fields of papers citing papers by Igor F. Tsigelny

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Igor F. Tsigelny. 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 Igor F. Tsigelny. The network helps show where Igor F. Tsigelny may publish in the future.

Co-authorship network of co-authors of Igor F. Tsigelny

This figure shows the co-authorship network connecting the top 25 collaborators of Igor F. Tsigelny. A scholar is included among the top collaborators of Igor F. Tsigelny 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 Igor F. Tsigelny. Igor F. Tsigelny 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.
Wang, Xiaoming, Valentina L. Kouznetsova, Santosh Kesari, & Igor F. Tsigelny. (2025). Developing a novel medulloblastoma diagnostic with miRNA biomarkers and machine learning. Child s Nervous System. 41(1). 221–221. 1 indexed citations
2.
Kouznetsova, Valentina L., et al.. (2025). Machine-learning diagnostics of breast cancer using piRNA biomarkers. Biomarkers. 30(2). 167–177. 1 indexed citations
3.
Tang, Sung‐Chun, Igor F. Tsigelny, Santosh Kesari, & Valentina L. Kouznetsova. (2025). miRNA biomarkers-based diagnosis of diffuse large B-cell lymphoma using machine learning. 8. 18–18.
4.
Kouznetsova, Valentina L., et al.. (2024). piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer. Molecules. 29(18). 4311–4311. 3 indexed citations
5.
Kouznetsova, Valentina L., et al.. (2024). Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning. Frontiers in Bioscience-Landmark. 29(1). 4–4. 9 indexed citations
6.
Kouznetsova, Valentina L., et al.. (2023). Small Molecule Decoys of Aggregation for Elimination of Aβ-Peptide Toxicity. ACS Chemical Neuroscience. 14(9). 1575–1584. 11 indexed citations
7.
Li, Eric, Valentina L. Kouznetsova, Santosh Kesari, & Igor F. Tsigelny. (2022). Machine-Learning Analysis of Single-Cell RNA-Seq Biomarkers in Gastric Cancer Caused By Helicobacter Pylori. 6(2). 1 indexed citations
8.
Kouznetsova, Valentina L., et al.. (2021). New Inhibitors of the P38 Mitogen-Activated Protein Kinase: Repurposing Existing Drugs with Deep Learning. Biointerface Research in Applied Chemistry. 12(4). 5384–5404. 2 indexed citations
9.
Kouznetsova, Valentina L., et al.. (2021). Molecular Mechanisms of Feline Cancers. 5(2). 1–29. 5 indexed citations
10.
11.
Kouznetsova, Valentina L., et al.. (2020). Deep-learning- and pharmacophore-based prediction of RAGE inhibitors. Physical Biology. 17(3). 36003–36003. 14 indexed citations
12.
Strickler, John H., Jonathan M. Loree, Leanne G. Ahronian, et al.. (2017). Genomic Landscape of Cell-Free DNA in Patients with Colorectal Cancer. Cancer Discovery. 8(2). 164–173. 196 indexed citations
13.
Kouznetsova, Valentina L., et al.. (2017). Glucose and Lipid Transporters Roles in Type 2 Diabetes. 3(5). 7 indexed citations
14.
Tsigelny, Igor F., Rajesh Mukthavaram, Valentina L. Kouznetsova, et al.. (2015). Multiple spatially related pharmacophores define small molecule inhibitors of OLIG2 in glioblastoma. Oncotarget. 8(14). 22370–22384. 19 indexed citations
15.
Tsigelny, Igor F., Yuriy Sharikov, Wolfgang Wrasidlo, et al.. (2012). Role of α‐synuclein penetration into the membrane in the mechanisms of oligomer pore formation. FEBS Journal. 279(6). 1000–1013. 139 indexed citations
16.
17.
Pezzementi, Leo, et al.. (2003). Amino acids defining the acyl pocket of an invertebrate cholinesterase. Comparative Biochemistry and Physiology Part B Biochemistry and Molecular Biology. 136(4). 813–832. 29 indexed citations
18.
Dostmann, Wolfgang R., et al.. (1999). Delineation of Selective Cyclic GMP-Dependent Protein Kinase Iα Substrate and Inhibitor Peptides Based on Combinatorial Peptide Libraries on Paper. Pharmacology & Therapeutics. 82(2-3). 373–387. 41 indexed citations
19.
Padilla, André, et al.. (1997). Solution structure of synthetic peptide inhibitor and substrate of cAMP‐dependent protein kinase. A study by 2D 1H NMR and molecular dynamics. Journal of Peptide Research. 49(3). 210–220. 8 indexed citations
20.
Tsigelny, Igor F. & Michael E. Baker. (1995). Structures important in mammalian 11β- and 17β-hydroxysteroid dehydrogenases. The Journal of Steroid Biochemistry and Molecular Biology. 55(5-6). 589–600. 19 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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