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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:

Deep learning in next-generation sequencing
Bertil Schmidt, Andreas Hildebrandt
Drug Discovery Today (2020) Vol. 26, Iss. 1, pp. 173-180
Open Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review
Omar Khatib, Simiao Ren, Jordan M. Malof, et al.
Advanced Functional Materials (2021) Vol. 31, Iss. 31
Open Access | Times Cited: 131
Next-Generation TB Vaccines: Progress, Challenges, and Prospects
Zhuang Li, Zhaoyang Ye, Linsheng Li, et al.
Vaccines (2023) Vol. 11, Iss. 8, pp. 1304-1304
Open Access | Times Cited: 59
Artificial intelligence for modelling infectious disease epidemics
Moritz U. G. Kraemer, Joseph L.-H. Tsui, Serina Chang, et al.
Nature (2025) Vol. 638, Iss. 8051, pp. 623-635
Closed Access | Times Cited: 6
Emerging applications of machine learning in genomic medicine and healthcare
Narjice Chafai, L. Bonizzi, Sara Botti, et al.
Critical Reviews in Clinical Laboratory Sciences (2023) Vol. 61, Iss. 2, pp. 140-163
Closed Access | Times Cited: 37
A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis
Kasmika Borah, Himanish Shekhar Das, Soumita Seth, et al.
Functional & Integrative Genomics (2024) Vol. 24, Iss. 5
Closed Access | Times Cited: 12
Leveraging deep learning to improve vaccine design
Andrew P. Hederman, Margaret E. Ackerman
Trends in Immunology (2023) Vol. 44, Iss. 5, pp. 333-344
Open Access | Times Cited: 19
Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning
Claudia Coronnello, Maria Giovanna Francipane
Stem Cell Reviews and Reports (2021) Vol. 18, Iss. 2, pp. 559-569
Open Access | Times Cited: 38
Application of deep learning in cancer epigenetics through DNA methylation analysis
Maryam Yassi, Aniruddha Chatterjee, Matthew Parry
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Open Access | Times Cited: 14
A scoping review on deep learning for next-generation RNA-Seq. data analysis
Diksha Pandey, Onkara Perumal P.
Functional & Integrative Genomics (2023) Vol. 23, Iss. 2
Closed Access | Times Cited: 13
Application of deep learning technique in next generation sequence experiments
Su Özgür, Mehmet Orman
Journal Of Big Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 13
En masse evaluation of RNA guides (EMERGe) for ADARs
Prince J Salvador, Natalie M Dugan, Randall Ouye, et al.
Methods in enzymology on CD-ROM/Methods in enzymology (2025), pp. 131-152
Closed Access
Unveiling the ghost: machine learning’s impact on the landscape of virology
Sebastian Bowyer, David J. Allen, Nicholas Furnham
Journal of General Virology (2025) Vol. 106, Iss. 1
Closed Access
The Application of artificial intelligence in periprosthetic joint infection
Pengcheng Li, Weisheng Yan, Runkai Zhao, et al.
Journal of Advanced Research (2025)
Open Access
DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data
Kasmika Borah, Himanish Shekhar Das, Ram Kaji Budhathoki, et al.
Briefings in Bioinformatics (2025) Vol. 26, Iss. 2
Open Access
Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases
Xuening Tang, Yudi Zhang, Hao Zhang, et al.
Clinical Reviews in Allergy & Immunology (2024) Vol. 66, Iss. 3, pp. 376-400
Closed Access | Times Cited: 3
Recent Advancement and Challenges in Deep Learning, Big Data in Bioinformatics
Ajay Sharma, Raj Kumar
Studies in big data (2022), pp. 251-284
Closed Access | Times Cited: 13
A Review of Cross-Disciplinary Approaches for the Identification of Novel Industrially Relevant Plastic-Degrading Enzymes
Josephine Herbert, Angela H. Beckett, Samuel C. Robson
Sustainability (2022) Vol. 14, Iss. 23, pp. 15898-15898
Open Access | Times Cited: 12
Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis
Sagar Dholariya, Ragini Singh, Amit Sonagra, et al.
Critical Reviews™ in Oncogenesis (2023) Vol. 28, Iss. 2, pp. 11-44
Closed Access | Times Cited: 7
Harnessing Machine Learning Potential for Personalised Drug Design and Overcoming Drug Resistance
Mohammed Ageeli Hakami
Journal of drug targeting (2024) Vol. 32, Iss. 8, pp. 918-930
Closed Access | Times Cited: 2
Deep learning for predicting 16S rRNA gene copy number
Jiazheng Miao, Tianlai Chen, Mustafa Mısır, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Deep learning prediction of attention-deficit hyperactivity disorder in African Americans by copy number variation
Yichuan Liu, Hui‐Qi Qu, Xiao Chang, et al.
Experimental Biology and Medicine (2021) Vol. 246, Iss. 21, pp. 2317-2323
Open Access | Times Cited: 13
Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning
Byungjo Lee, Min Kyoung Shin, Jung Sun Yoo, et al.
Frontiers in Microbiology (2022) Vol. 13
Open Access | Times Cited: 9
Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences
Bahrad A. Sokhansanj, Gail Rosen
mSystems (2022) Vol. 7, Iss. 2
Open Access | Times Cited: 8
Demystifying the Discussion of Sequencing Panel Size in Oncology Genetic Testing
Cecília Durães, Carla Pereira Gomes, José Luís Costa, et al.
European Medical Journal (2022), pp. 68-77
Open Access | Times Cited: 8
A split-and-merge deep learning approach for phenotype prediction
Wei-Heng Huang, Yu‐Chung Wei
Frontiers in Bioscience-Landmark (2022) Vol. 27, Iss. 3, pp. 078-078
Open Access | Times Cited: 7
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