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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:

XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data
Sheetal Rajpal, Ankit Rajpal, Arpita Saggar, et al.
Expert Systems with Applications (2023) Vol. 225, pp. 120130-120130
Closed Access | Times Cited: 21

Showing 21 citing articles:

Can ChatGPT provide intelligent diagnoses? A comparative study between predictive models and ChatGPT to define a new medical diagnostic bot
Loredana Caruccio, Stefano Cirillo, Giuseppe Polese, et al.
Expert Systems with Applications (2023) Vol. 235, pp. 121186-121186
Open Access | Times Cited: 87
Guaranteeing Correctness in Black-Box Machine Learning: A Fusion of Explainable AI and Formal Methods for Healthcare Decision-Making
Nadia Khan, Muhammad Nauman, Ahmad Almadhor, et al.
IEEE Access (2024) Vol. 12, pp. 90299-90316
Open Access | Times Cited: 11
Deep learning approaches to detect breast cancer: a comprehensive review
Amir Mohammad Sharafaddini, Kiana Kouhpah Esfahani, N. Mansouri
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 10
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Emrullah Şahin, Naciye Nur Arslan, Durmuş Özdemir
Neural Computing and Applications (2024)
Closed Access | Times Cited: 10
Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review
Youssef Alaaeldin Ali Mohamed, Bee Luan Khoo, Mohd Shahrimie Mohd Asaari, et al.
International Journal of Medical Informatics (2024) Vol. 193, pp. 105689-105689
Closed Access | Times Cited: 9
Enhancing transparency of omics data analysis with the Evolutionary Multi-Test Tree and Relative Expression
Marcin Czajkowski, Krzysztof Jurczuk, Marek Krętowski
Expert Systems with Applications (2025), pp. 127131-127131
Closed Access | Times Cited: 1
Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence
Elizabeth Gutierrez‐Chakraborty, Debaditya Chakraborty, Debodipta Das, et al.
Expert Systems with Applications (2024) Vol. 252, pp. 124239-124239
Open Access | Times Cited: 4
An Explainable AI Framework for Comparative Analysis of the Model Explanations in Breast Cancer Prediction
Ghazaleh Emadi, Ana Belén Gil González
Lecture notes in networks and systems (2025), pp. 21-30
Closed Access
Improved Breast Cancer Classification Approach Using Hybrid Deep Learning Strategies for Tumor Segmentation
Anitha Venugopal, S. Murugavalli, A. Ameelia Roseline
Sensing and Imaging (2024) Vol. 25, Iss. 1
Closed Access | Times Cited: 3
SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data
Gian Maria Zaccaria, Nicola Altini, Giuseppe Mezzolla, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 244, pp. 107966-107966
Open Access | Times Cited: 7
Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence
Karthik Sekaran, Rinku Polachirakkal Varghese, Shibu Krishnan, et al.
Frontiers in Bioscience-Landmark (2024) Vol. 29, Iss. 6, pp. 220-220
Open Access | Times Cited: 2
Explainable Artificial Intelligence Methods for Breast Cancer Recognition
Robertas Damaševičius
Deleted Journal (2024) Vol. 1, Iss. 3, pp. 25-25
Open Access | Times Cited: 2
Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts
Muhammad Tahir, Mahboobeh Norouzi, Shehroz S. Khan, et al.
Computers in Biology and Medicine (2024) Vol. 183, pp. 109302-109302
Open Access | Times Cited: 2
Enhancing cardiovascular risk assessment with advanced data balancing and domain knowledge-driven explainability
Fan Yang, Yanan Qiao, Petr Hájek, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124886-124886
Open Access | Times Cited: 1
AI-driven transcriptomic encoders: From explainable models to accurate, sample-independent cancer diagnostics
Danilo Croce, Artem Smirnov, Luigi Tiburzi, et al.
Expert Systems with Applications (2024) Vol. 258, pp. 125126-125126
Open Access | Times Cited: 1
Discovering Novel Prognostic Biomarkers of Hepatocellular Carcinoma using eXplainable Artificial Intelligence
Elizabeth Gutierrez‐Chakraborty, Debaditya Chakraborty, Debodipta Das, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
Liangrui Pan, Pengfei Rong, Dazheng Liu, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023), pp. 904-909
Open Access | Times Cited: 2
NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset
Min Li, Yuheng Cai, Mingzhuang Zhang, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 254, pp. 108291-108291
Closed Access
Cancer Detection and Treatment Using Explainable AI
Pratik Rawal, Dev Ahuja, Madan Lal Saini, et al.
International Journal of Scientific Research and Modern Technology. (2024) Vol. 3, Iss. 9, pp. 1-8
Closed Access
EpiBrCan-Lite: A Lightweight Deep Learning model for Breast Cancer Subtype Classification using Epigenomic Data
Punam Bedi, Surbhi Rani, Bhavna Gupta, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 260, pp. 108553-108553
Closed Access
Identification of gene-level methylation for disease prediction
Jisha Augustine, A. S. Jereesh
Interdisciplinary Sciences Computational Life Sciences (2023) Vol. 15, Iss. 4, pp. 678-695
Closed Access
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