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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:

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl
Communications in computer and information science (2020), pp. 417-431
Open Access | Times Cited: 411

Showing 26-50 of 411 citing articles:

Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization
Zhonglin Cao, Omid Barati Farimani, Janghoon Ock, et al.
Nano Letters (2024) Vol. 24, Iss. 10, pp. 2953-2960
Open Access | Times Cited: 18
Prediction models for the complication incidence and survival rate of dental implants—a systematic review and critical appraisal
Yuanxi Zhu, Mi Du, Ping Li, et al.
International Journal of Implant Dentistry (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 2
Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors
Mie Andersen, Karsten Reuter
Accounts of Chemical Research (2021) Vol. 54, Iss. 12, pp. 2741-2749
Closed Access | Times Cited: 82
Artificial intelligence in early drug discovery enabling precision medicine
Fabio Boniolo, Emilio Dorigatti, Alexander J. Ohnmacht, et al.
Expert Opinion on Drug Discovery (2021) Vol. 16, Iss. 9, pp. 991-1007
Open Access | Times Cited: 80
Choice modelling in the age of machine learning - Discussion paper
Sander van Cranenburgh, Shenhao Wang, Akshay Vij, et al.
Journal of Choice Modelling (2021) Vol. 42, pp. 100340-100340
Open Access | Times Cited: 73
A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions
Talal A. A. Abdullah, Mohd Soperi Mohd Zahid, Waleed Ali
Symmetry (2021) Vol. 13, Iss. 12, pp. 2439-2439
Open Access | Times Cited: 69
Shapley variable importance cloud for interpretable machine learning
Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, et al.
Patterns (2022) Vol. 3, Iss. 4, pp. 100452-100452
Open Access | Times Cited: 68
Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges
Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, et al.
IEEE Access (2021) Vol. 9, pp. 36879-36897
Open Access | Times Cited: 67
Ethical considerations for precision psychiatry: A roadmap for research and clinical practice
Paolo Fusar‐Poli, Mirko Manchia, Nikolaos Koutsouleris, et al.
European Neuropsychopharmacology (2022) Vol. 63, pp. 17-34
Open Access | Times Cited: 65
Design of Experiments and machine learning for product innovation: A systematic literature review
Rosa Arboretti, Riccardo Ceccato, Luca Pegoraro, et al.
Quality and Reliability Engineering International (2021) Vol. 38, Iss. 2, pp. 1131-1156
Closed Access | Times Cited: 60
Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review
Yehualashet Megersa Ayano, Friedhelm Schwenker, Bisrat Derebssa Dufera, et al.
Diagnostics (2022) Vol. 13, Iss. 1, pp. 111-111
Open Access | Times Cited: 60
Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones
Yuan Yuan, Chengwei Li, Xiaolei Geng, et al.
Environment International (2022) Vol. 170, pp. 107574-107574
Open Access | Times Cited: 59
Sustainability in Wood Products: A New Perspective for Handling Natural Diversity
Mark Schubert, Guido Panzarasa, Ingo Burgert
Chemical Reviews (2022) Vol. 123, Iss. 5, pp. 1889-1924
Closed Access | Times Cited: 55
Improving hydrologic models for predictions and process understanding using neural ODEs
Marvin Höge, Andreas Scheidegger, Marco Baity‐Jesi, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 19, pp. 5085-5102
Open Access | Times Cited: 48
Interpretable machine learning for real estate market analysis
Felix Lorenz, Jonas Willwersch, Marcelo Cajias, et al.
Real Estate Economics (2022) Vol. 51, Iss. 5, pp. 1178-1208
Open Access | Times Cited: 43
Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit
R. I. Hamilton, Panagiotis N. Papadopoulos
IEEE Transactions on Power Systems (2023) Vol. 39, Iss. 1, pp. 1384-1397
Open Access | Times Cited: 38
An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
Cai Yang, Mohammad Zoynul Abedin, Hongwei Zhang, et al.
Annals of Operations Research (2023)
Open Access | Times Cited: 36
Long short-term memory models of water quality in inland water environments
JongCheol Pyo, Yakov Pachepsky, Soobin Kim, et al.
Water Research X (2023) Vol. 21, pp. 100207-100207
Open Access | Times Cited: 31
Gene selection with Game Shapley Harris hawks optimizer for cancer classification
Sana Afreen, Ajay Kumar Bhurjee, Rabia Musheer Aziz
Chemometrics and Intelligent Laboratory Systems (2023) Vol. 242, pp. 104989-104989
Closed Access | Times Cited: 29
It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems
Dragutin Petković
IEEE Transactions on Technology and Society (2023) Vol. 4, Iss. 1, pp. 46-53
Open Access | Times Cited: 28
Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models
Songhua Hu, Chenfeng Xiong, Peng Chen, et al.
Transportation Research Part A Policy and Practice (2023) Vol. 174, pp. 103743-103743
Closed Access | Times Cited: 27
AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics
Fa Li, Qing Zhu, W. J. Riley, et al.
Geoscientific model development (2023) Vol. 16, Iss. 3, pp. 869-884
Open Access | Times Cited: 24
Trust in AI: progress, challenges, and future directions
Saleh Afroogh, Ali Akbari, Emmie Malone, et al.
Humanities and Social Sciences Communications (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 14
A machine learning model that outperforms conventional global subseasonal forecast models
Lei Chen, Xiaohui Zhong, Hao Li, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 13
Artificial intelligence-driven real-world battery diagnostics
Jingyuan Zhao, Xudong Qu, Yuyan Wu, et al.
Energy and AI (2024) Vol. 18, pp. 100419-100419
Open Access | Times Cited: 12

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