David Friedecký

1.9k total citations
113 papers, 1.3k citations indexed

About

David Friedecký is a scholar working on Molecular Biology, Physiology and Clinical Biochemistry. According to data from OpenAlex, David Friedecký has authored 113 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Molecular Biology, 24 papers in Physiology and 20 papers in Clinical Biochemistry. Recurrent topics in David Friedecký's work include Metabolomics and Mass Spectrometry Studies (24 papers), Biochemical and Molecular Research (20 papers) and Metabolism and Genetic Disorders (20 papers). David Friedecký is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (24 papers), Biochemical and Molecular Research (20 papers) and Metabolism and Genetic Disorders (20 papers). David Friedecký collaborates with scholars based in Czechia, Slovakia and Netherlands. David Friedecký's co-authors include Tomáš Adam, Edgar Faber, Lukáš Najdekr, Petr Barták, Hana Janečková, Radana Brumarová, Miroslav Strnad, Jitka Široká, Karel Koberna and Anna Ligasová and has published in prestigious journals such as Nature Communications, Blood and Bioinformatics.

In The Last Decade

David Friedecký

102 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Friedecký Czechia 21 632 166 155 144 139 113 1.3k
Tomáš Adam Czechia 23 780 1.2× 220 1.3× 77 0.5× 261 1.8× 233 1.7× 97 1.6k
Damiana Pieragostino Italy 30 1.5k 2.3× 99 0.6× 215 1.4× 250 1.7× 138 1.0× 88 2.6k
Sabrina Malvagia Italy 26 545 0.9× 562 3.4× 212 1.4× 92 0.6× 77 0.6× 55 1.6k
Kenji Nakayama Japan 23 521 0.8× 43 0.3× 235 1.5× 120 0.8× 55 0.4× 114 1.7k
Paul Voziyan United States 29 956 1.5× 747 4.5× 324 2.1× 96 0.7× 56 0.4× 60 2.3k
Keith L. Clay United States 26 697 1.1× 78 0.5× 311 2.0× 223 1.5× 40 0.3× 56 2.1k
Juan Carlos García‐Cañaveras Spain 21 1.1k 1.8× 67 0.4× 182 1.2× 185 1.3× 144 1.0× 34 2.0k
Tsuyoshi Kobayashi Japan 30 587 0.9× 73 0.4× 297 1.9× 75 0.5× 57 0.4× 118 2.6k
Helena Idborg Sweden 21 645 1.0× 35 0.2× 80 0.5× 272 1.9× 115 0.8× 57 1.3k
Makiko Suzuki Japan 15 724 1.1× 72 0.4× 440 2.8× 37 0.3× 94 0.7× 63 1.5k

Countries citing papers authored by David Friedecký

Since Specialization
Citations

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

Fields of papers citing papers by David Friedecký

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Friedecký

This figure shows the co-authorship network connecting the top 25 collaborators of David Friedecký. A scholar is included among the top collaborators of David Friedecký 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 David Friedecký. David Friedecký 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.
Idkowiak, Jakub, Jonas Dehairs, Xander Spotbeen, et al.. (2025). Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data. Nature Communications. 16(1). 8714–8714.
2.
Friedecký, David, et al.. (2025). Ceramide-based risk score: A novel laboratory tool for cardiovascular risk stratification in hyperuricemia and gout. Vascular Pharmacology. 159. 107495–107495.
3.
Menšíková, Kateřina, et al.. (2025). Comparison of inflammatory biomarker levels in neurodegenerative proteinopathies: a case-control study. Journal of Neural Transmission. 132(6). 811–826. 1 indexed citations
4.
Brüning, Thomas, et al.. (2024). Sex-dependent efficacy of sphingosine-1-phosphate receptor agonist FTY720 in mitigating Huntington’s disease. Pharmacological Research. 211. 107557–107557. 1 indexed citations
5.
Sedlák, František, et al.. (2024). Parallel Metabolomics and Lipidomics of a PSMA/GCPII Deficient Mouse Model Reveal Alteration of NAAG Levels and Brain Lipid Composition. ACS Chemical Neuroscience. 15(7). 1342–1355. 5 indexed citations
6.
Jahn, P., et al.. (2024). Horse with myopathy caused by consumption of box elder tree seedlings in the Czech Republic. Equine Veterinary Education. 37(5).
7.
Hényková, Eva, Kateřina Menšíková, David Friedecký, et al.. (2024). Patients with Neurodegenerative Proteinopathies Exhibit Altered Tryptophan Metabolism in the Serum and Cerebrospinal Fluid. ACS Chemical Neuroscience. 15(3). 582–592. 13 indexed citations
8.
Friedecký, David, et al.. (2024). Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors. Bioinformatics Advances. 5(1). vbaf073–vbaf073.
9.
Menšíková, Kateřina, Michaela Kaiserová, Hana Přikrylová Vranová, et al.. (2023). Cerebrospinal fluid and blood serum biomarkers in neurodegenerative proteinopathies: A prospective, open, cross‐correlation study. Journal of Neurochemistry. 167(2). 168–182. 12 indexed citations
10.
Wilms, Ines, Javier Palarea‐Albaladejo, Peter Filzmoser, et al.. (2023). Principal balances of compositional data for regression and classification using partial least squares. Journal of Chemometrics. 37(12). 2 indexed citations
11.
12.
Friedecký, David, Radana Brumarová, Markéta Pavlı́ková, et al.. (2023). Alterations in lipidome profiles distinguish early-onset hyperuricemia, gout, and the effect of urate-lowering treatment. Arthritis Research & Therapy. 25(1). 234–234. 22 indexed citations
13.
Bekárek, Vojtěch, et al.. (2023). Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine. Clinical Chemistry and Laboratory Medicine (CCLM). 61(11). 2017–2027. 7 indexed citations
14.
Najdekr, Lukáš, et al.. (2023). Clinical lipidomics in the era of the big data. Clinical Chemistry and Laboratory Medicine (CCLM). 61(4). 587–598. 16 indexed citations
15.
Brumarová, Radana, et al.. (2022). Combined Targeted and Untargeted Profiling of HeLa Cells Deficient in Purine De Novo Synthesis. Metabolites. 12(3). 241–241. 3 indexed citations
16.
Hulsen, Tim, David Friedecký, Harald Renz, et al.. (2022). From big data to better patient outcomes. Clinical Chemistry and Laboratory Medicine (CCLM). 61(4). 580–586. 19 indexed citations
17.
Friedecký, David, et al.. (2021). Novel LC-MS tools for diagnosing inborn errors of metabolism. Microchemical Journal. 170. 106654–106654. 7 indexed citations
18.
Procházková, Jana, et al.. (2021). Evaluation of the Determination of Dabigatran, Rivaroxaban, and Apixaban in Lupus Anticoagulant-Positive Patients. Diagnostics. 11(11). 2027–2027. 4 indexed citations
19.
Barešová, Veronika, et al.. (2018). Mass spectrometric analysis of purine de novo biosynthesis intermediates. PLoS ONE. 13(12). e0208947–e0208947. 18 indexed citations
20.
Friedecký, David, et al.. (2011). Analysis of Nucleotides in Dry Blood Spots. Chemické listy. 105(3). 1 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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