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OpenAlex Citation Counts

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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:

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study
Sivan Kinreich, Jacquelyn L. Meyers, Adi Maron‐Katz, et al.
Molecular Psychiatry (2019) Vol. 26, Iss. 4, pp. 1133-1141
Open Access | Times Cited: 56

Showing 1-25 of 56 citing articles:

Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality
María S. García‐Gutiérrez, Francisco Navarrete, F. Sala, et al.
Frontiers in Psychiatry (2020) Vol. 11
Open Access | Times Cited: 234
Multimodal machine learning in precision health: A scoping review
Adrienne Kline, Hanyin Wang, Yikuan Li, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 216
Machine-learning approaches to substance-abuse research: emerging trends and their implications
Elan Barenholtz, Nicole D. Fitzgerald, William Edward Hahn
Current Opinion in Psychiatry (2020) Vol. 33, Iss. 4, pp. 334-342
Closed Access | Times Cited: 57
Multimodal-based machine learning approach to classify features of internet gaming disorder and alcohol use disorder: A sensor-level and source-level resting-state electroencephalography activity and neuropsychological study
Jiyoon Lee, Myeong Seop Song, So Young Yoo, et al.
Comprehensive Psychiatry (2024) Vol. 130, pp. 152460-152460
Open Access | Times Cited: 7
Enhancing Aviation Safety through AI-Driven Mental Health Management for Pilots and Air Traffic Controllers
Krešimir Ćosić, Siniša Popović, Brenda K. Wiederhold
Cyberpsychology Behavior and Social Networking (2024) Vol. 27, Iss. 8, pp. 588-598
Closed Access | Times Cited: 7
Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
Sivan Kinreich, Vivia V. McCutcheon, Fazil Alıev, et al.
Translational Psychiatry (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 36
The Collaborative Study on the Genetics of Alcoholism: Overview
Arpana Agrawal, Sarah J. Brislin, Kathleen K. Bucholz, et al.
Genes Brain & Behavior (2023) Vol. 22, Iss. 5
Open Access | Times Cited: 15
Pre-existing DNA methylation signatures in the prefrontal cortex of alcohol-naïve nonhuman primates define neural vulnerability for future risky ethanol consumption
Rita Cervera‐Juanes, Kip D. Zimmerman, Larry Wilhelm, et al.
Neurobiology of Disease (2025), pp. 106886-106886
Open Access
The MLSE-SCAM architecture combines with the improved DRSN-TIC model for Raman spectroscopy small-sample data learning
Zhiyuan Cheng, Fangfang Chen, Enguang Zuo, et al.
Expert Systems with Applications (2025) Vol. 279, pp. 127462-127462
Closed Access
Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures
Chella Kamarajan, Babak A. Ardekani, Ashwini K. Pandey, et al.
Brain Sciences (2020) Vol. 10, Iss. 2, pp. 115-115
Open Access | Times Cited: 37
The collaborative study on the genetics of alcoholism: Sample and clinical data
Danielle M. Dick, Emily Balcke, Vivia V. McCutcheon, et al.
Genes Brain & Behavior (2023) Vol. 22, Iss. 5
Open Access | Times Cited: 12
Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures
Chella Kamarajan, Babak A. Ardekani, Ashwini K. Pandey, et al.
Behavioral Sciences (2020) Vol. 10, Iss. 3, pp. 62-62
Open Access | Times Cited: 29
Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction
Dardo Tomasi, Corinde E. Wiers, Peter Manza, et al.
Cerebral Cortex (2021) Vol. 31, Iss. 7, pp. 3254-3265
Open Access | Times Cited: 25
Clinical, environmental, and genetic risk factors for substance use disorders: characterizing combined effects across multiple cohorts
Peter B. Barr, Morgan N. Driver, Sally I‐Chun Kuo, et al.
Molecular Psychiatry (2022) Vol. 27, Iss. 11, pp. 4633-4641
Open Access | Times Cited: 17
The collaborative study on the genetics of alcoholism: Brain function
Jacquelyn L. Meyers, Sarah J. Brislin, Chella Kamarajan, et al.
Genes Brain & Behavior (2023) Vol. 22, Iss. 5
Open Access | Times Cited: 10
Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data
Xianglian Meng, Qingpeng Wei, Meng Li, et al.
Genes (2022) Vol. 13, Iss. 5, pp. 837-837
Open Access | Times Cited: 15
Predicting the Risk of Alcohol Use Disorder Using Machine Learning: A Systematic Literature Review
Ali Ebrahimi, Uffe Kock Wiil, Thomas Schmidt, et al.
IEEE Access (2021) Vol. 9, pp. 151697-151712
Open Access | Times Cited: 18
Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance
Eric J. Barnett, Daniel G. Onete, Asif Salekin, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023) Vol. 21, Iss. 1, pp. 169-177
Open Access | Times Cited: 7
Classifying Different Types of Smokers and Drinkers by Analyzing Body Signals using Machine Learning
Rakib Hasan, Ferdous Hasan, Md Mehedi Hasan, et al.
2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (2024), pp. 1060-1064
Closed Access | Times Cited: 2
The role of weighting adjustment for attrition in longitudinal trajectory modeling: a simulation study
Brady T. West, Yajuan Si, Yueying Hu, et al.
Communications in Statistics - Simulation and Computation (2024), pp. 1-23
Closed Access | Times Cited: 2
Internal consistency and test–retest reliability of the P3 event‐related potential (ERP) elicited by alcoholic and non‐alcoholic beverage pictures
Roberto U. Cofresí, Thomas M. Piasecki, Greg Hajcak, et al.
Psychophysiology (2021) Vol. 59, Iss. 2
Open Access | Times Cited: 16
Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population
Md. Ariful Islam Arif, Saiful Islam Sany, Farah Sharmin, et al.
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (2021) Vol. 11, Iss. 5, pp. 4471-4471
Open Access | Times Cited: 13
Genomic Machine Learning Meta-regression: Insights on Associations of Study Features with Reported Model Performance
Eric J. Barnett, Daniel G. Onete, Asif Salekin, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 9
Using machine learning to predict heavy drinking during outpatient alcohol treatment
Walter Roberts, Yize Zhao, Terril L. Verplaetse, et al.
Alcoholism Clinical and Experimental Research (2022) Vol. 46, Iss. 4, pp. 657-666
Open Access | Times Cited: 9
Electroencephalography Microstate Alterations in Otogenic Vertigo: A Potential Disease Marker
Yi‐Ni Li, Wen Lu, Jie Li, et al.
Frontiers in Aging Neuroscience (2022) Vol. 14
Open Access | Times Cited: 9
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