Anna Gambin

3.3k total citations
95 papers, 1.7k citations indexed

About

Anna Gambin is a scholar working on Molecular Biology, Spectroscopy and Genetics. According to data from OpenAlex, Anna Gambin has authored 95 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Molecular Biology, 22 papers in Spectroscopy and 20 papers in Genetics. Recurrent topics in Anna Gambin's work include Metabolomics and Mass Spectrometry Studies (15 papers), Advanced Proteomics Techniques and Applications (15 papers) and Genomic variations and chromosomal abnormalities (14 papers). Anna Gambin is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (15 papers), Advanced Proteomics Techniques and Applications (15 papers) and Genomic variations and chromosomal abnormalities (14 papers). Anna Gambin collaborates with scholars based in Poland, United States and Belgium. Anna Gambin's co-authors include Błażej Miasojedow, Michał Startek, Neo Christopher Chung, Mateusz Krzysztof Łącki, Tomasz Gambin, Paweł Stankiewicz, Piotr Dittwald, Jerzy Tiuryn, Mikołaj Rybiński and Bartosz A. Grzybowski and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Anna Gambin

92 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anna Gambin Poland 22 894 348 273 190 181 95 1.7k
Angelo Facchiano Italy 36 1.7k 1.9× 268 0.8× 331 1.2× 329 1.7× 213 1.2× 166 3.6k
Mónica Chagoyen Spain 21 1.5k 1.6× 291 0.8× 151 0.6× 117 0.6× 129 0.7× 56 2.2k
Hiroyuki Kurata Japan 31 2.2k 2.5× 163 0.5× 192 0.7× 166 0.9× 142 0.8× 163 3.0k
Shaun M. Kandathil United Kingdom 15 981 1.1× 395 1.1× 80 0.3× 187 1.0× 319 1.8× 24 2.3k
Debasis Dash India 26 1.2k 1.4× 200 0.6× 291 1.1× 68 0.4× 108 0.6× 114 2.1k
Jacob de Vlieg Netherlands 19 1.5k 1.7× 158 0.5× 257 0.9× 484 2.5× 140 0.8× 37 2.5k
Mark Williams United Kingdom 19 1.8k 2.0× 131 0.4× 311 1.1× 176 0.9× 85 0.5× 32 2.4k
Hsuan‐Cheng Huang Taiwan 35 2.5k 2.8× 500 1.4× 202 0.7× 297 1.6× 74 0.4× 160 4.1k
Σοφία Κοσσίδα Greece 22 1.2k 1.3× 156 0.4× 124 0.5× 183 1.0× 47 0.3× 100 2.0k
Thomas Wilhelm Germany 27 1.5k 1.6× 91 0.3× 188 0.7× 185 1.0× 97 0.5× 129 2.9k

Countries citing papers authored by Anna Gambin

Since Specialization
Citations

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

Fields of papers citing papers by Anna Gambin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anna Gambin

This figure shows the co-authorship network connecting the top 25 collaborators of Anna Gambin. A scholar is included among the top collaborators of Anna Gambin 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 Gambin. Anna Gambin 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.
Gambin, Anna, et al.. (2024). Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology. Methods. 224. 1–9. 2 indexed citations
2.
3.
Miasojedow, Błażej, et al.. (2023). Magnetstein: An Open-Source Tool for Quantitative NMR Mixture Analysis Robust to Low Resolution, Distorted Lineshapes, and Peak Shifts. Analytical Chemistry. 96(1). 188–196. 7 indexed citations
4.
Krueger, Tyll, Krzysztof Gogolewski, Marcin Bodych, et al.. (2022). Risk assessment of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes. SHILAP Revista de lepidopterología. 2(1). 23–23. 37 indexed citations
5.
Łącki, Mateusz Krzysztof, et al.. (2017). Leaf and Plant Age Affects Photosynthetic Performance and Photoprotective Capacity. PLANT PHYSIOLOGY. 175(4). 1634–1648. 115 indexed citations
6.
Dittwald, Piotr, et al.. (2017). Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?. Scientific Reports. 7(1). 3582–3582. 110 indexed citations
7.
Dharmadhikari, Avinash V., Tomasz Gambin, Przemysław Szafrański, et al.. (2014). Molecular and clinical analyses of 16q24.1 duplications involving FOXF1 identify an evolutionarily unstable large minisatellite. BMC Medical Genetics. 15(1). 128–128. 21 indexed citations
8.
Sen, Partha, Avinash V. Dharmadhikari, Tadeusz Majewski, et al.. (2014). Comparative Analyses of Lung Transcriptomes in Patients with Alveolar Capillary Dysplasia with Misalignment of Pulmonary Veins and in Foxf1 Heterozygous Knockout Mice. PLoS ONE. 9(4). e94390–e94390. 25 indexed citations
9.
Bartnik, Magdalena, Beata Nowakowska, Katarzyna Derwińska, et al.. (2013). Application of array comparative genomic hybridization in 256 patients with developmental delay or intellectual disability. Journal of Applied Genetics. 55(1). 125–144. 26 indexed citations
10.
Gambin, Tomasz, Paweł Stankiewicz, Maciej Sykulski, & Anna Gambin. (2013). Functional performance of aCGH design for clinical cytogenetics. Computers in Biology and Medicine. 43(6). 775–785. 1 indexed citations
11.
Gambin, Anna, Agata Charzyńska, Aleksandra Ellert‐Miklaszewska, & Mikołaj Rybiński. (2013). Computational models of the JAK1/2-STAT1 signaling. PubMed. 2(3). e24672–e24672. 24 indexed citations
12.
Startek, Michał, Sławomir Lasota, Maciej Sykulski, et al.. (2012). Efficient alternatives to PSI-BLAST. Bulletin of the Polish Academy of Sciences Technical Sciences. 60(3). 495–505. 1 indexed citations
13.
Charzyńska, Agata, et al.. (2012). Sensitivity analysis of mathematical models of signalling pathways. BioTechnologia. 93(3). 11 indexed citations
14.
Dittwald, Piotr, Jerzy Ostrowski, Jakub Karczmarski, & Anna Gambin. (2012). Inferring serum proteolytic activity from LC-MS/MS data. BMC Bioinformatics. 13(S5). S7–S7. 12 indexed citations
15.
Gambin, Tomasz, Paweł Stankiewicz, & Anna Gambin. (2011). A stable density approach to probe selection for a custom aCGH design. BioTechnologia. 92(3). 283–295. 1 indexed citations
16.
Gambin, Anna, Ewa Szczurek, Janusz Dutkowski, Magdalena Bakun, & Michał Dadlez. (2009). Classification of peptide mass fingerprint data by novel no-regret boosting method. Computers in Biology and Medicine. 39(5). 460–473. 6 indexed citations
17.
Bolikowski, Łukasz & Anna Gambin. (2007). New Metrics for Phylogenies. Fundamenta Informaticae. 78(2). 199–216. 3 indexed citations
18.
Dojer, Norbert, Anna Gambin, Andrzej Mizera, Bartek Wilczyński, & Jerzy Tiuryn. (2006). Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics. 7(1). 249–249. 102 indexed citations
19.
Gambin, Anna, et al.. (2002). Contextual Alignment of Biological Sequences. 18(2). 116–127. 9 indexed citations
20.
Gambin, Anna, et al.. (2002). Contextual alignment of biological sequences (Extended abstract). Bioinformatics. 18(suppl_2). S116–S127. 5 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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