Onur Türkoğlu

969 total citations
38 papers, 719 citations indexed

About

Onur Türkoğlu is a scholar working on Molecular Biology, Obstetrics and Gynecology and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Onur Türkoğlu has authored 38 papers receiving a total of 719 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 15 papers in Obstetrics and Gynecology and 12 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Onur Türkoğlu's work include Metabolomics and Mass Spectrometry Studies (12 papers), Pregnancy and preeclampsia studies (10 papers) and Birth, Development, and Health (7 papers). Onur Türkoğlu is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (12 papers), Pregnancy and preeclampsia studies (10 papers) and Birth, Development, and Health (7 papers). Onur Türkoğlu collaborates with scholars based in United States, Canada and United Kingdom. Onur Türkoğlu's co-authors include Ray Bahado‐Singh, Stewart F. Graham, David S. Wishart, Ali Yılmaz, Beom-Soo Han, Praveen Kumar, Uppala Radhakrishna, Trent C. Bjorndahl, Kunle Odunsi and Olivier Chevallier and has published in prestigious journals such as PLoS ONE, Scientific Reports and Brain Research.

In The Last Decade

Onur Türkoğlu

36 papers receiving 714 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Onur Türkoğlu United States 17 294 232 205 110 104 38 719
Ali Yılmaz United States 15 311 1.1× 98 0.4× 127 0.6× 42 0.4× 86 0.8× 32 684
Kirsten Hartil United States 12 405 1.4× 121 0.5× 288 1.4× 53 0.5× 88 0.8× 16 790
Ioannis Papageorgiou Greece 16 212 0.7× 82 0.4× 123 0.6× 42 0.4× 51 0.5× 35 727
Barbara Sigala United Kingdom 8 321 1.1× 65 0.3× 182 0.9× 61 0.6× 181 1.7× 9 689
Kris Meurrens Switzerland 15 102 0.3× 158 0.7× 212 1.0× 77 0.7× 30 0.3× 21 638
M Congiu Australia 17 197 0.7× 329 1.4× 131 0.6× 26 0.2× 199 1.9× 46 951
Flávia Fonseca Bloise Brazil 16 184 0.6× 62 0.3× 75 0.4× 24 0.2× 47 0.5× 31 609
Francesco Palmas Italy 14 174 0.6× 60 0.3× 123 0.6× 22 0.2× 71 0.7× 23 509
Stan Spence United States 14 467 1.6× 64 0.3× 84 0.4× 28 0.3× 26 0.3× 23 748
Pablo Sanjurjo Spain 20 318 1.1× 57 0.2× 237 1.2× 20 0.2× 70 0.7× 67 1.1k

Countries citing papers authored by Onur Türkoğlu

Since Specialization
Citations

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

Fields of papers citing papers by Onur Türkoğlu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Onur Türkoğlu

This figure shows the co-authorship network connecting the top 25 collaborators of Onur Türkoğlu. A scholar is included among the top collaborators of Onur Türkoğlu 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 Onur Türkoğlu. Onur Türkoğlu 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.
2.
Bahado‐Singh, Ray, et al.. (2025). Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence. Scientific Reports. 15(1). 2060–2060. 3 indexed citations
3.
Türkoğlu, Onur, et al.. (2024). Untargeted Metabolomic Biomarker Discovery for the Detection of Ectopic Pregnancy. International Journal of Molecular Sciences. 25(19). 10333–10333. 2 indexed citations
4.
Türkoğlu, Onur, et al.. (2024). Metabolomic prediction of severe maternal and newborn complications in preeclampsia. Metabolomics. 20(3). 56–56. 3 indexed citations
5.
Türkoğlu, Onur, et al.. (2023). Fetal effects of mild maternal COVID-19 infection: metabolomic profiling of cord blood. Metabolomics. 19(4). 41–41. 3 indexed citations
6.
Bahado‐Singh, Ray, Sangeetha Vishweswaraiah, Onur Türkoğlu, Stewart F. Graham, & Uppala Radhakrishna. (2023). Alzheimer’s Precision Neurology: Epigenetics of Cytochrome P450 Genes in Circulating Cell-Free DNA for Disease Prediction and Mechanism. International Journal of Molecular Sciences. 24(3). 2876–2876. 15 indexed citations
7.
Türkoğlu, Onur, et al.. (2023). Neurocutaneous Disorders in Pregnancy. Obstetrical & Gynecological Survey. 78(10). 606–619.
8.
Akyol, Sümeyya, et al.. (2023). Metabolomics: An Emerging “Omics” Platform for Systems Biology and Its Implications for Huntington Disease Research. Metabolites. 13(12). 1203–1203. 6 indexed citations
9.
Alhousseini, Ali, et al.. (2022). Does Maternal SARS-CoV-2 Infection or SARS-CoV-2 Vaccination Trigger an Inflammatory Response in the Fetus? A Prospective Cohort Study. Gynecologic and Obstetric Investigation. 87(3-4). 219–225. 6 indexed citations
10.
Graham, Stewart F., Onur Türkoğlu, Ali Yılmaz, et al.. (2020). Targeted metabolomics highlights perturbed metabolism in the brain of autism spectrum disorder sufferers. Metabolomics. 16(5). 59–59. 20 indexed citations
11.
Yılmaz, Ali, Halil Bişğin, Onur Türkoğlu, et al.. (2019). Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction. PLoS ONE. 14(4). e0214121–e0214121. 48 indexed citations
12.
Türkoğlu, Onur, İsmail Mert, Praveen Kumar, et al.. (2019). Metabolomic identification of novel diagnostic biomarkers in ectopic pregnancy. Metabolomics. 15(11). 143–143. 9 indexed citations
13.
Bahado‐Singh, Ray, Sangeetha Vishweswaraiah, Buket Aydas, et al.. (2019). Artificial Intelligence and the detection of pediatric concussion using epigenomic analysis. Brain Research. 1726. 146510–146510. 18 indexed citations
14.
Bahado‐Singh, Ray, Argyro Syngelaki, Rupsari Mandal, et al.. (2018). First-trimester metabolomic prediction of stillbirth. The Journal of Maternal-Fetal & Neonatal Medicine. 32(20). 3435–3441. 7 indexed citations
15.
Baker, Dawn, Onur Türkoğlu, Rose E. Callahan, et al.. (2018). Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma. Metabolomics. 14(8). 105–105. 15 indexed citations
16.
Özdemir, Elif, et al.. (2018). Diminished ovarian reserve in patients with psoriasis. Taiwanese Journal of Obstetrics and Gynecology. 57(2). 227–230. 17 indexed citations
17.
Bahado‐Singh, Ray, Liona C. Poon, Ali Yılmaz, et al.. (2017). Integrated Proteomic and Metabolomic prediction of Term Preeclampsia. Scientific Reports. 7(1). 16189–16189. 37 indexed citations
18.
Bahado‐Singh, Ray, Argyro Syngelaki, Rupsari Mandal, et al.. (2016). Metabolomic determination of pathogenesis of late-onset preeclampsia. The Journal of Maternal-Fetal & Neonatal Medicine. 30(6). 658–664. 44 indexed citations
19.
Graham, Stewart F., et al.. (2016). Metabolomic profiling of brain from infants who died from Sudden Infant Death Syndrome reveals novel predictive biomarkers. Journal of Perinatology. 37(1). 91–97. 29 indexed citations
20.
Bahado‐Singh, Ray, Stewart F. Graham, Beom-Soo Han, et al.. (2016). Serum metabolomic markers for traumatic brain injury: a mouse model. Metabolomics. 12(6). 23 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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