Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

High-throughput deep learning variant effect prediction with Sequence UNET
Alistair S. Dunham, Pedro Beltrão, Mohammed AlQuraishi
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 24

Showing 24 citing articles:

Genome-wide prediction of disease variant effects with a deep protein language model
Nadav Brandes, Grant Goldman, Charlotte H. Wang, et al.
Nature Genetics (2023) Vol. 55, Iss. 9, pp. 1512-1522
Open Access | Times Cited: 221
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold
ACS Central Science (2024) Vol. 10, Iss. 2, pp. 226-241
Open Access | Times Cited: 76
Artificial Intelligence for Cardiovascular Care—Part 1: Advances
Pierre Elias, Sneha S. Jain, Timothy J. Poterucha, et al.
Journal of the American College of Cardiology (2024) Vol. 83, Iss. 24, pp. 2472-2486
Closed Access | Times Cited: 29
Variant effect predictor correlation with functional assays is reflective of clinical classification performance
Benjamin Livesey, Joseph A. Marsh
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 9
Are protein language models the new universal key?
Konstantin Weißenow, Burkhard Rost
Current Opinion in Structural Biology (2025) Vol. 91, pp. 102997-102997
Open Access | Times Cited: 1
microGWAS: a computational pipeline to perform large-scale bacterial genome-wide association studies
Judit Burgaya, Bamu F. Damaris, Jenny Fiebig, et al.
Microbial Genomics (2025) Vol. 11, Iss. 2
Open Access | Times Cited: 1
Deciphering “the language of nature”: A transformer-based language model for deleterious mutations in proteins
Theodore T. Jiang, Fang Li, Kai Wang
The Innovation (2023) Vol. 4, Iss. 5, pp. 100487-100487
Open Access | Times Cited: 20
Nearest neighbor search on embeddings rapidly identifies distant protein relations
Konstantin Schütze, Michael Heinzinger, Martin Steinegger, et al.
Frontiers in Bioinformatics (2022) Vol. 2
Open Access | Times Cited: 28
Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors
Yu-Jen Lin, Arul S. Menon, Zhiqiang Hu, et al.
Human Genomics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 6
Variant Effect Prediction in the Age of Machine Learning
Yana Bromberg, R. Prabakaran, Anowarul Kabir, et al.
Cold Spring Harbor Perspectives in Biology (2024) Vol. 16, Iss. 7, pp. a041467-a041467
Closed Access | Times Cited: 4
Advancing variant effect prediction using protein language models
Benjamin Livesey, Joseph A. Marsh
Nature Genetics (2023) Vol. 55, Iss. 9, pp. 1426-1427
Open Access | Times Cited: 10
Variant effect predictor correlation with functional assays is reflective of clinical classification performance
Benjamin Livesey, Joseph A. Marsh
Genome biology (2025) Vol. 26, Iss. 1
Open Access
Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale
Jinyuan Sun, Tong Zhu, Yinglu Cui, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 8
Understanding variants of unknown significance: the computational frontier
Xi Fu, Raúl Rabadán
The Oncologist (2024)
Open Access | Times Cited: 2
Artificial Intelligence Learns Protein Prediction
Michael Heinzinger, Burkhard Rost
Cold Spring Harbor Perspectives in Biology (2024) Vol. 16, Iss. 9, pp. a041458-a041458
Closed Access | Times Cited: 2
Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects
Xiaoyu Wang, Fuyi Li, Yiwen Zhang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 2
FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction
Matsvei Tsishyn, Gabriel Cia, Pauline Hermans, et al.
Human Genomics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 1
Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction
Yang Qu, Zitong Niu, Qiaojiao Ding, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 22, pp. 16496-16496
Open Access | Times Cited: 3
FiTMuSiC: Leveraging structural and (co)evolutionary data for protein fitness prediction
Matsvei Tsishyn, Gabriel Cia, Pauline Hermans, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 1
Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors
Yu-Jen Lin, Arul S. Menon, Zhiqiang Hu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Deep learning in variant detection and annotation
Shaban Ahmad, Aman Bashar, Kushagra Khanna, et al.
Elsevier eBooks (2024), pp. 383-396
Closed Access
Progress on the development of prediction tools for detecting disease causing mutations in proteins
M. Michael Gromiha, Medha Pandey, A. Kulandaisamy, et al.
Computers in Biology and Medicine (2024) Vol. 185, pp. 109510-109510
Closed Access
microGWAS: a computational pipeline to perform large scale bacterial genome-wide association studies
Judit Burgaya, Bamu F. Damaris, Jenny Fiebig, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
Page 1

Scroll to top