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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:

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
Raquel Rodríguez-Pérez, Jürgen Bajorath
Journal of Computer-Aided Molecular Design (2020) Vol. 34, Iss. 10, pp. 1013-1026
Open Access | Times Cited: 436

Showing 1-25 of 436 citing articles:

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine
Jihong Min, Jiaobing Tu, Changhao Xu, et al.
Chemical Reviews (2023) Vol. 123, Iss. 8, pp. 5049-5138
Open Access | Times Cited: 311
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Yang Chen, Chang Ren, Yuefei Jia, et al.
Acta Materialia (2021) Vol. 222, pp. 117431-117431
Closed Access | Times Cited: 192
Machine learning predicts and optimizes hydrothermal liquefaction of biomass
Alireza Shafizadeh, Hossein Shahbeig, Mohammad Hossein Nadian, et al.
Chemical Engineering Journal (2022) Vol. 445, pp. 136579-136579
Closed Access | Times Cited: 140
Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects
Md. Milon Islam, Sheikh Nooruddin, Fakhri Karray, et al.
Computers in Biology and Medicine (2022) Vol. 149, pp. 106060-106060
Open Access | Times Cited: 129
An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation
Ali El Bilali, Taleb Abdeslam, Ayoub Nafii, et al.
Journal of Environmental Management (2022) Vol. 327, pp. 116890-116890
Closed Access | Times Cited: 99
Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare
Lara Marques, Bárbara Costa, Mariana Pereira, et al.
Pharmaceutics (2024) Vol. 16, Iss. 3, pp. 332-332
Open Access | Times Cited: 70
Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes
Hossein Shahbeik, Shahin Rafiee, Alireza Shafizadeh, et al.
Renewable Energy (2022) Vol. 199, pp. 1078-1092
Closed Access | Times Cited: 69
New avenues in artificial-intelligence-assisted drug discovery
Carmen Cerchia, Antonio Lavecchia
Drug Discovery Today (2023) Vol. 28, Iss. 4, pp. 103516-103516
Open Access | Times Cited: 57
On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points
Arkaprava Banerjee, Kunal Roy
Chemical Research in Toxicology (2023) Vol. 36, Iss. 3, pp. 446-464
Closed Access | Times Cited: 48
Advancing material property prediction: using physics-informed machine learning models for viscosity
Alex K. Chew, Matthew Sender, Zachary Kaplan, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 20
From physical activity patterns to cognitive status: development and validation of novel digital biomarkers for cognitive assessment in older adults
Lingjie Fan, Fengyi Wang, Junhan Zhao, et al.
International Journal of Behavioral Nutrition and Physical Activity (2025) Vol. 22, Iss. 1
Open Access | Times Cited: 2
Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation
Pan Pan, Yichao Li, Yongjiu Xiao, et al.
Journal of Medical Internet Research (2020) Vol. 22, Iss. 11, pp. e23128-e23128
Open Access | Times Cited: 114
New insights into hydrogen uptake on porous carbon materials via explainable machine learning
Muhammad Irfan Maulana Kusdhany, Stephen Matthew Lyth
Carbon (2021) Vol. 179, pp. 190-201
Open Access | Times Cited: 96
Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
Minsu Kim, Dominik Brunner, Gerrit Kuhlmann
Remote Sensing of Environment (2021) Vol. 264, pp. 112573-112573
Open Access | Times Cited: 75
Prediction and evaluation of spatial distributions of ozone and urban heat island using a machine learning modified land use regression method
Li Han, Jingyuan Zhao, Yuejing Gao, et al.
Sustainable Cities and Society (2021) Vol. 78, pp. 103643-103643
Closed Access | Times Cited: 71
A Goal Scoring Probability Model for Shots Based on Synchronized Positional and Event Data in Football (Soccer)
Gabriel Anzer, Pascal Bauer
Frontiers in Sports and Active Living (2021) Vol. 3
Open Access | Times Cited: 69
Explainable Machine Learning for Property Predictions in Compound Optimization
Raquel Rodríguez-Pérez, Jürgen Bajorath
Journal of Medicinal Chemistry (2021) Vol. 64, Iss. 24, pp. 17744-17752
Closed Access | Times Cited: 60
An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images
Liang Han, Guijun Yang, Xiaodong Yang, et al.
Computers and Electronics in Agriculture (2022) Vol. 194, pp. 106804-106804
Closed Access | Times Cited: 59
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
Hakan Başağaoğlu, Debaditya Chakraborty, Cesar Do Lago, et al.
Water (2022) Vol. 14, Iss. 8, pp. 1230-1230
Open Access | Times Cited: 55
Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation
Ryo Fujimori, Keibun Liu, Shoko Soeno, et al.
JMIR Formative Research (2022) Vol. 6, Iss. 6, pp. e36501-e36501
Open Access | Times Cited: 52
Interpretation of Structure–Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence
Tobias Harren, Hans Matter, Gerhard Heßler, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 3, pp. 447-462
Closed Access | Times Cited: 45
SHAP Value-Based Feature Importance Analysis for Short-Term Load Forecasting
Yong-Geon Lee, Jae-Young Oh, Dong-Sung Kim, et al.
Journal of Electrical Engineering and Technology (2022) Vol. 18, Iss. 1, pp. 579-588
Closed Access | Times Cited: 44
Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification
Olatomiwa O. Bifarin
PLoS ONE (2023) Vol. 18, Iss. 5, pp. e0284315-e0284315
Open Access | Times Cited: 36
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models
Joseph D. Janizek, Ayse B. Dincer, Safiye Çelik, et al.
Nature Biomedical Engineering (2023) Vol. 7, Iss. 6, pp. 811-829
Closed Access | Times Cited: 28
Explainability and white box in drug discovery
Kevser Kübra Kırboğa, Sumra Wajid Abbasi, Ecir Uğur Küçüksille
Chemical Biology & Drug Design (2023) Vol. 102, Iss. 1, pp. 217-233
Closed Access | Times Cited: 26
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