Aleksandr Ianevski

6.0k total citations · 4 hit papers
54 papers, 2.8k citations indexed

About

Aleksandr Ianevski is a scholar working on Molecular Biology, Computational Theory and Mathematics and Immunology. According to data from OpenAlex, Aleksandr Ianevski has authored 54 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 15 papers in Computational Theory and Mathematics and 14 papers in Immunology. Recurrent topics in Aleksandr Ianevski's work include Computational Drug Discovery Methods (15 papers), SARS-CoV-2 and COVID-19 Research (10 papers) and Influenza Virus Research Studies (6 papers). Aleksandr Ianevski is often cited by papers focused on Computational Drug Discovery Methods (15 papers), SARS-CoV-2 and COVID-19 Research (10 papers) and Influenza Virus Research Studies (6 papers). Aleksandr Ianevski collaborates with scholars based in Finland, Norway and Estonia. Aleksandr Ianevski's co-authors include Tero Aittokallio, Anil K. Giri, Liye He, Jing Tang, Denis E. Kainov, Magnar Bjørås, Tanel Tenson, Krister Wennerberg, Valentyn Oksenych and Petter I. Andersen and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Blood.

In The Last Decade

Aleksandr Ianevski

51 papers receiving 2.8k citations

Hit Papers

align trajectories

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.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

2020 502 citations Aleksandr Ianevski, Anil K. Giri et al. Nucleic Acids Research profile →
SynergyFinder: a web application for analyzing drug combination dose–response matrix data

2017 353 citations Aleksandr Ianevski, Liye He et al. Bioinformatics profile →
SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples

2022 335 citations Aleksandr Ianevski, Anil K. Giri et al. Nucleic Acids Research profile →
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

2022 298 citations Aleksandr Ianevski, Anil K. Giri et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aleksandr Ianevski Finland	23	1.5k	557	507	373	335	54	2.8k
Yaron Turpaz United States	14	2.1k 1.4×	350 0.6×	228 0.4×	296 0.8×	261 0.8×	20	3.5k
Xiaoyun Lu China	34	2.1k 1.4×	828 1.5×	294 0.6×	262 0.7×	186 0.6×	159	3.6k
David A. Ostrov United States	42	1.9k 1.3×	737 1.3×	656 1.3×	176 0.5×	1.0k 3.0×	141	5.1k
Sara J. Buhrlage United States	26	1.9k 1.3×	717 1.3×	226 0.4×	119 0.3×	429 1.3×	66	2.9k
Mario R. Ehlers United States	33	1.7k 1.2×	565 1.0×	465 0.9×	205 0.5×	960 2.9×	77	4.6k
Keunsoo Kang South Korea	31	2.2k 1.5×	618 1.1×	337 0.7×	245 0.7×	612 1.8×	149	3.7k
Curtis R. Chong United States	26	1.9k 1.3×	1.2k 2.2×	355 0.7×	446 1.2×	235 0.7×	45	4.1k
Cord Naujokat Germany	28	1.3k 0.9×	955 1.7×	477 0.9×	143 0.4×	539 1.6×	50	3.0k
Mark G. Anderson United States	25	2.0k 1.4×	985 1.8×	201 0.4×	123 0.3×	487 1.5×	47	3.4k
Xin Hu United States	28	1.5k 1.0×	272 0.5×	512 1.0×	380 1.0×	743 2.2×	109	3.5k

Countries citing papers authored by Aleksandr Ianevski

Since Specialization

Citations

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

Fields of papers citing papers by Aleksandr Ianevski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aleksandr Ianevski

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

All Works

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20 of 20 papers shown

Jokinen, Emmi, Essi Laajala, Shady Adnan Awad, et al.. (2025). High-throughput drug screening identifies SMAC mimetics as enhancers of NK-cell cytotoxicity in chronic myeloid leukemia. Blood. 145(15). 1670–1686. 2 indexed citations

Ianevski, Aleksandr, Hilde Lysvand, Ole Solheim, et al.. (2025). JAK inhibitors remove innate immune barriers facilitating viral propagation. PubMed. 2(2). ugaf017–ugaf017.

Chen, Yingjia, Liye He, Aleksandr Ianevski, et al.. (2025). A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia. Cancer Research. 85(14). 2753–2768. 4 indexed citations

Ianevski, Aleksandr, Wojciech Senkowski, Daria Bulanova, et al.. (2024). Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones. Nature Communications. 15(1). 8579–8579. 13 indexed citations

Ianevski, Aleksandr, et al.. (2024). ScType enables fast and accurate cell type identification from spatial transcriptomics data. Bioinformatics. 40(7). 9 indexed citations

Glaß, Markus, Waseem Lone, Aleksandr Ianevski, et al.. (2023). The miR-141/200c-STAT4 Axis Contributes to Leukemogenesis by Enhancing Cell Proliferation in T-PLL. Cancers. 15(9). 2527–2527. 5 indexed citations

Kolderup, Anette, Marina Vaysburd, Donna L. Mallery, et al.. (2023). A pan-SARS-CoV-2-specific soluble angiotensin-converting enzyme 2-albumin fusion engineered for enhanced plasma half-life and needle-free mucosal delivery. PNAS Nexus. 2(12). pgad403–pgad403. 5 indexed citations

Ianevski, Aleksandr, Yosuke Kodama, Hiroyuki Tomita, et al.. (2023). Mo1238 GASTRIC INTESTINAL METAPLASIA VS GASTRIC ADENOCARCINOMA IN PATIENTS: DISTINCT PHENOTYPIC TRAITS IN TRANSCRIPTOMICS. Gastroenterology. 164(6). S–796. 1 indexed citations

Wagoner, Jessica, Andreas B. den Hartigh, Julia M. Sidorova, et al.. (2022). Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2–host protein–protein interaction network. Briefings in Bioinformatics. 23(6). 9 indexed citations

10.

Ajmera, Ishan, Amelia Henry, Ando M. Radanielson, et al.. (2022). Integrated root phenotypes for improved rice performance under low nitrogen availability. Plant Cell & Environment. 45(3). 805–822. 35 indexed citations

11.

Wagoner, Jessica, Tien-Ying Hsiang, Aleksandr Ianevski, et al.. (2022). Combinations of Host- and Virus-Targeting Antiviral Drugs Confer Synergistic Suppression of SARS-CoV-2. Microbiology Spectrum. 10(5). e0333122–e0333122. 34 indexed citations

12.

Ianevski, Aleksandr, et al.. (2022). DrugVirus.info 2.0: an integrative data portal for broad-spectrum antivirals (BSA) and BSA-containing drug combinations (BCCs). Nucleic Acids Research. 50(W1). W272–W275. 23 indexed citations

13.

Ianevski, Aleksandr, Anil K. Giri, & Tero Aittokallio. (2022). Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nature Communications. 13(1). 1246–1246. 298 indexed citations breakdown →

14.

Ianevski, Aleksandr, Rouan Yao, Eva Žusinaite, et al.. (2022). Mono- and combinational drug therapies for global viral pandemic preparedness. iScience. 25(4). 104112–104112. 20 indexed citations

15.

Wagoner, Jessica, Elizabeth A. Nelson, Qinfeng Huang, et al.. (2021). Inhibition of Arenaviruses by Combinations of Orally Available Approved Drugs. Antimicrobial Agents and Chemotherapy. 65(4). 30 indexed citations

16.

Ianevski, Aleksandr, Jenni Lahtela, Komal Kumar Javarappa, et al.. (2021). Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Science Advances. 7(8). 27 indexed citations

17.

Brück, Oscar, Helena Hohtari, Aleksandr Ianevski, et al.. (2021). Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS. Blood Cancer Discovery. 2(3). 238–249. 36 indexed citations

18.

White, Judith M., Joshua T. Schiffer, Rachel Bender Ignacio, et al.. (2021). Drug Combinations as a First Line of Defense against Coronaviruses and Other Emerging Viruses. mBio. 12(6). e0334721–e0334721. 42 indexed citations

19.

Dufva, Olli, Pilvi Maliniemi, Aleksandr Ianevski, et al.. (2019). Integrated drug profiling and CRISPR screening identify essential pathways for CAR T-cell cytotoxicity. Blood. 135(9). 597–609. 157 indexed citations

20.

Ianevski, Aleksandr, Anil K. Giri, Prson Gautam, et al.. (2019). Prediction of drug combination effects with a minimal set of experiments. Nature Machine Intelligence. 1(12). 568–577. 106 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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