Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition
Tobias Bruderer, Emmanuel Varesio, Gérard Hopfgartner
Journal of Chromatography B (2017) Vol. 1071, pp. 3-10
Closed Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics
Ivana Blaženović, Tobias Kind, Jian Ji, et al.
Metabolites (2018) Vol. 8, Iss. 2, pp. 31-31
Open Access | Times Cited: 610
The METLIN small molecule dataset for machine learning-based retention time prediction
Xavier Domingo-Almenara, Carlos Guijas, Elizabeth Billings, et al.
Nature Communications (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 195
Annotation: A Computational Solution for Streamlining Metabolomics Analysis
Xavier Domingo-Almenara, J. Rafael Montenegro-Burke, H. Paul Benton, et al.
Analytical Chemistry (2017) Vol. 90, Iss. 1, pp. 480-489
Open Access | Times Cited: 151
Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?
François Fenaille, Pierre Barbier Saint-Hilaire, Kathleen Rousseau, et al.
Journal of Chromatography A (2017) Vol. 1526, pp. 1-12
Closed Access | Times Cited: 121
Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology
Ruohong Wang, Yandong Yin, Zheng‐Jiang Zhu
Analytical and Bioanalytical Chemistry (2019) Vol. 411, Iss. 19, pp. 4349-4357
Closed Access | Times Cited: 121
Prediction of Analyte Retention Time in Liquid Chromatography
Paul R. Haddad, Maryam Taraji, Roman Szücs
Analytical Chemistry (2020) Vol. 93, Iss. 1, pp. 228-256
Closed Access | Times Cited: 107
Current status of retention time prediction in metabolite identification
Michael Witting, Sebastian Böcker
Journal of Separation Science (2020) Vol. 43, Iss. 9-10, pp. 1746-1754
Open Access | Times Cited: 100
Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular Metabolites
Xinjie Zhao, Zhongda Zeng, Aiming Chen, et al.
Analytical Chemistry (2018) Vol. 90, Iss. 12, pp. 7635-7643
Closed Access | Times Cited: 98
RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification
Jun Xue, Bingyi Wang, Hongchao Ji, et al.
Bioinformatics (2024) Vol. 40, Iss. 3
Open Access | Times Cited: 17
New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells
Sneha Couvillion, Ying Zhu, Gabe Nagy, et al.
The Analyst (2018) Vol. 144, Iss. 3, pp. 794-807
Open Access | Times Cited: 81
SWATH data independent acquisition mass spectrometry for metabolomics
R. F. Bonner, Gérard Hopfgartner
TrAC Trends in Analytical Chemistry (2018) Vol. 120, pp. 115278-115278
Closed Access | Times Cited: 76
Challenges in Identifying the Dark Molecules of Life
Marı́a Eugenia Monge, James N. Dodds, Erin Baker, et al.
Annual Review of Analytical Chemistry (2019) Vol. 12, Iss. 1, pp. 177-199
Open Access | Times Cited: 70
Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography
Ruth Amos, Paul R. Haddad, Roman Szücs, et al.
TrAC Trends in Analytical Chemistry (2018) Vol. 105, pp. 352-359
Closed Access | Times Cited: 60
Recent advances in non-targeted screening analysis using liquid chromatography - high resolution mass spectrometry to explore new biomarkers for human exposure
Ze‐Qin Guo, Sheng‐Yu Huang, Jianhua Wang, et al.
Talanta (2020) Vol. 219, pp. 121339-121339
Closed Access | Times Cited: 58
Quantitative structure retention relationship (QSRR) modelling for Analytes’ retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance
Theodoros Liapikos, Ch. Zisi, Dritan Kodra, et al.
Journal of Chromatography B (2022) Vol. 1191, pp. 123132-123132
Closed Access | Times Cited: 37
Retention Time Prediction with Message-Passing Neural Networks
Sergey Osipenko, Е. Н. Николаев, Yury Kostyukevich
Separations (2022) Vol. 9, Iss. 10, pp. 291-291
Open Access | Times Cited: 31
SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis
Michel Raetz, R. F. Bonner, Gérard Hopfgartner
Metabolomics (2020) Vol. 16, Iss. 6
Closed Access | Times Cited: 50
Mass spectrometry-based metabolomics for clinical study: Recent progresses and applications
Jun Ding, Yu‐Qi Feng
TrAC Trends in Analytical Chemistry (2022) Vol. 158, pp. 116896-116896
Closed Access | Times Cited: 24
Current trends in chromatographic prediction using artificial intelligence and machine learning
Yash Raj Singh, Darshil B. Shah, Mangesh Kulkarni, et al.
Analytical Methods (2023) Vol. 15, Iss. 23, pp. 2785-2797
Closed Access | Times Cited: 15
Machine learning to predict retention time of small molecules in nano-HPLC
Sergey Osipenko, Inga Bashkirova, Sergey Sosnin, et al.
Analytical and Bioanalytical Chemistry (2020) Vol. 412, Iss. 28, pp. 7767-7776
Closed Access | Times Cited: 38
Metabolite Annotation and Identification
Joanna Godzień, Alberto Gil-de-la-Fuente, Abraham Otero, et al.
Comprehensive analytical chemistry (2018), pp. 415-445
Closed Access | Times Cited: 38
Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS
Chao Feng, Qian Xu, Xinlei Qiu, et al.
Chemosphere (2020) Vol. 271, pp. 129447-129447
Closed Access | Times Cited: 32
Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity
Yash Raj Singh, Darshil B. Shah, Dilip Maheshwari, et al.
Critical Reviews in Analytical Chemistry (2023) Vol. 54, Iss. 8, pp. 3559-3569
Closed Access | Times Cited: 10
ROASMI: accelerating small molecule identification by repurposing retention data
Fang-Yuan Sun, Yinghao Yin, Huijun Liu, et al.
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
The Role and Choice of Molecular Descriptors for Predicting Retention Times in HPLC: A Comprehensive Review
Elena Bandini, Ardiana Kajtazi, Roman Szücs, et al.
TrAC Trends in Analytical Chemistry (2025), pp. 118207-118207
Closed Access
Page 1 - Next Page

Scroll to top