Oleksandr Narykov

683 total citations
13 papers, 321 citations indexed

About

Oleksandr Narykov is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Oleksandr Narykov has authored 13 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 2 papers in Materials Chemistry. Recurrent topics in Oleksandr Narykov's work include Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (6 papers) and Bioinformatics and Genomic Networks (6 papers). Oleksandr Narykov is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (6 papers) and Bioinformatics and Genomic Networks (6 papers). Oleksandr Narykov collaborates with scholars based in United States, Netherlands and Italy. Oleksandr Narykov's co-authors include Dmitry Korkin, Senbao Lu, Suhas Srinivasan, Ming Liu, Ziyang Gao, Hongzhu Cui, Thomas Brettin, Austin Clyde, Alexander Partin and Yitan Zhu and has published in prestigious journals such as Nature Communications, Nature Neuroscience and Bioinformatics.

In The Last Decade

Oleksandr Narykov

12 papers receiving 316 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Oleksandr Narykov United States	7	162	146	67	26	23	13	321
Karoline Leopold Canada	7	195 1.2×	237 1.6×	51 0.8×	32 1.2×	12 0.5×	7	419
Yu-An Kung Taiwan	8	214 1.3×	143 1.0×	31 0.5×	25 1.0×	16 0.7×	13	386
Stephanie Portelli Australia	10	253 1.6×	102 0.7×	56 0.8×	18 0.7×	7 0.3×	24	371
Jonathan Borowsky United States	5	287 1.8×	112 0.8×	108 1.6×	15 0.6×	25 1.1×	7	391
Taniya Bhardwaj India	10	136 0.8×	128 0.9×	35 0.5×	27 1.0×	10 0.4×	18	273
Feng-Yi Ke Taiwan	5	190 1.2×	190 1.3×	27 0.4×	49 1.9×	16 0.7×	7	426
Nicholas J. Rettko United States	6	185 1.1×	150 1.0×	19 0.3×	32 1.2×	13 0.6×	8	321
Danyi Ao China	7	133 0.8×	180 1.2×	15 0.2×	61 2.3×	11 0.5×	10	364
Geng Gao United States	7	308 1.9×	125 0.9×	58 0.9×	88 3.4×	6 0.3×	7	470
Ryan S. McCool United States	5	322 2.0×	170 1.2×	33 0.5×	18 0.7×	16 0.7×	6	462

Countries citing papers authored by Oleksandr Narykov

Since Specialization

Citations

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

Fields of papers citing papers by Oleksandr Narykov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Oleksandr Narykov

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

All Works

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13 of 13 papers shown

Narykov, Oleksandr, Thomas Brettin, Yvonne A. Evrard, et al.. (2025). Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting. Briefings in Bioinformatics. 26(2).

Prjibelski, Andrey D., Wen Hu, Lieke Michielsen, et al.. (2025). A spatial long-read approach at near-single-cell resolution reveals developmental regulation of splicing and polyadenylation sites in distinct cortical layers and cell types. Nature Communications. 16(1). 8093–8093. 1 indexed citations

Joglekar, Anoushka, Wen Hu, Bei Zhang, et al.. (2024). Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain. Nature Neuroscience. 27(6). 1051–1063. 29 indexed citations

Zhu, Yitan, Thomas Brettin, Alexander Partin, et al.. (2024). A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening. Cancers. 16(3). 530–530. 3 indexed citations

Partin, Alexander, Thomas Brettin, Yitan Zhu, et al.. (2023). Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Frontiers in Medicine. 10. 1086097–1086097. 59 indexed citations

Partin, Alexander, Thomas Brettin, Yitan Zhu, et al.. (2023). Abstract 5380: Systematic evaluation and comparison of drug response prediction models: a case study of prediction generalization across cell lines datasets. Cancer Research. 83(7_Supplement). 5380–5380. 1 indexed citations

Narykov, Oleksandr, Thomas Brettin, Yvonne A. Evrard, et al.. (2023). Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models. Cancers. 16(1). 50–50. 6 indexed citations

Pezeshkian, Weria, Fabian Grünewald, Oleksandr Narykov, et al.. (2023). Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling. Structure. 31(4). 492–503.e7. 29 indexed citations

Narykov, Oleksandr, Thomas Brettin, Yvonne A. Evrard, et al.. (2023). Entropy-Based Regularization on Deep Learning Models for Anti-Cancer Drug Response Prediction. 121–122. 1 indexed citations

10.

Narykov, Oleksandr, Nathan Johnson, & Dmitry Korkin. (2021). Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning. Cell Reports. 37(8). 110045–110045. 6 indexed citations

11.

Narykov, Oleksandr, Suhas Srinivasan, & Dmitry Korkin. (2021). Computational protein modeling and the next viral pandemic. Nature Methods. 18(5). 444–445. 7 indexed citations

12.

Srinivasan, Suhas, Hongzhu Cui, Ziyang Gao, et al.. (2020). Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins. Viruses. 12(4). 360–360. 172 indexed citations

13.

Narykov, Oleksandr, et al.. (2019). DISPOT: a simple knowledge-based protein domain interaction statistical potential. Bioinformatics. 35(24). 5374–5378. 7 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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