Anna Cichońska

1.8k total citations · 1 hit paper
17 papers, 829 citations indexed

About

Anna Cichońska is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Anna Cichońska has authored 17 papers receiving a total of 829 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 4 papers in Genetics. Recurrent topics in Anna Cichońska's work include Computational Drug Discovery Methods (8 papers), Bioinformatics and Genomic Networks (6 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Anna Cichońska is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Bioinformatics and Genomic Networks (6 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Anna Cichońska collaborates with scholars based in Finland, United States and United Kingdom. Anna Cichońska's co-authors include Heli Julkunen, Juho Rousu, Peter Würtz, Tero Aittokallio, Tapio Pahikkala, P. Eline Slagboom, Veikko Salomaa, Antti J. Kangas, Balaguru Ravikumar and Pasi Soininen and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Bioinformatics.

In The Last Decade

Anna Cichońska

16 papers receiving 823 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.

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank

2023 216 citations Heli Julkunen, Anna Cichońska 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
Anna Cichońska Finland	14	476	245	172	110	92	17	829
Heli Julkunen Finland	6	272 0.6×	102 0.4×	106 0.6×	76 0.7×	79 0.9×	9	513
Salvatore Alaimo Italy	16	506 1.1×	211 0.9×	42 0.2×	149 1.4×	42 0.5×	49	948
Alison H. Harrill United States	19	387 0.8×	90 0.4×	79 0.5×	178 1.6×	43 0.5×	33	1.1k
Yun Gyu Song South Korea	16	353 0.7×	229 0.9×	41 0.2×	72 0.7×	17 0.2×	37	1.0k
Rachel Huddart United States	11	349 0.7×	90 0.4×	279 1.6×	25 0.2×	38 0.4×	14	1.0k
Thomas Wendl Germany	14	243 0.5×	74 0.3×	99 0.6×	38 0.3×	21 0.2×	24	998
Judith Farrés Spain	13	465 1.0×	134 0.5×	37 0.2×	144 1.3×	87 0.9×	25	852
Reagan Kelly United States	16	512 1.1×	215 0.9×	87 0.5×	74 0.7×	70 0.8×	27	1.1k
Syril Pettit United States	22	820 1.7×	210 0.9×	45 0.3×	25 0.2×	54 0.6×	47	1.7k
Jean-Pierre Provost France	6	608 1.3×	66 0.3×	48 0.3×	84 0.8×	126 1.4×	7	806

Countries citing papers authored by Anna Cichońska

Since Specialization

Citations

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

Fields of papers citing papers by Anna Cichońska

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anna Cichońska

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

All Works

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17 of 17 papers shown

Akinsanya, Karen, Mohammed AlQuraishi, Ann Boija, et al.. (2025). Redefining druggable targets with artificial intelligence. Nature Biotechnology. 43(9). 1416–1418. 1 indexed citations

Cichońska, Anna, Balaguru Ravikumar, & Rayees Rahman. (2024). AI for targeted polypharmacology: The next frontier in drug discovery. Current Opinion in Structural Biology. 84. 102771–102771. 27 indexed citations

Ravikumar, Balaguru, et al.. (2024). Leveraging multiple data types for improved compound-kinase bioactivity prediction. Nature Communications. 15(1). 7596–7596. 3 indexed citations

Julkunen, Heli, Anna Cichońska, Mika Tiainen, et al.. (2023). Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nature Communications. 14(1). 604–604. 216 indexed citations breakdown →

Trozzi, Francesco, et al.. (2023). Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures. Bioinformatics Advances. 3(1). vbad129–vbad129. 13 indexed citations

Cichońska, Anna, Charumathi Sabanayagam, Simon Nusinovici, et al.. (2022). Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations. The Journal of Clinical Endocrinology & Metabolism. 107(7). e2751–e2761. 16 indexed citations

Bell, Joshua A., Tom G. Richardson, Qin Wang, et al.. (2022). Effects of general and central adiposity on circulating lipoprotein, lipid, and metabolite levels in UK Biobank: A multivariable Mendelian randomization study. The Lancet Regional Health - Europe. 21. 100457–100457. 28 indexed citations

Sinnott-Armstrong, Nasa, Anna Cichońska, Heli Julkunen, et al.. (2022). Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation. eLife. 11. 21 indexed citations

Szedmák, Sándor, Haishan Wang, Tero Aittokallio, et al.. (2021). Modeling drug combination effects via latent tensor reconstruction. Bioinformatics. 37(Supplement_1). i93–i101. 14 indexed citations

10.

Julkunen, Heli, et al.. (2021). Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population. eLife. 10. 117 indexed citations

11.

Julkunen, Heli, Anna Cichońska, Prson Gautam, et al.. (2020). Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nature Communications. 11(1). 6136–6136. 75 indexed citations

12.

Cichońska, Anna, Tapio Pahikkala, Sándor Szedmák, et al.. (2018). Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinformatics. 34(13). i509–i518. 53 indexed citations

13.

Cichońska, Anna. (2018). Machine Learning for Systems Pharmacology. Aaltodoc (Aalto University).

14.

Cichońska, Anna, Balaguru Ravikumar, Elina Parri, et al.. (2017). Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors. PLoS Computational Biology. 13(8). e1005678–e1005678. 83 indexed citations

15.

Cichońska, Anna, Juho Rousu, Pekka Marttinen, et al.. (2016). metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics. 32(13). 1981–1989. 78 indexed citations

16.

Honeyborne, Isobella, Timothy D. McHugh, Anna Cichońska, et al.. (2016). Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy. BMC Medicine. 14(1). 68–68. 39 indexed citations

17.

Cichońska, Anna, Juho Rousu, & Tero Aittokallio. (2015). Identification of drug candidates and repurposing opportunities through compound–target interaction networks. Expert Opinion on Drug Discovery. 10(12). 1333–1345. 45 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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