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:

Representation learning applications in biological sequence analysis
Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 3198-3208
Open Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Informed training set design enables efficient machine learning-assisted directed protein evolution
Bruce J. Wittmann, Yisong Yue, Frances H. Arnold
Cell Systems (2021) Vol. 12, Iss. 11, pp. 1026-1045.e7
Open Access | Times Cited: 161
Applications of transformer-based language models in bioinformatics: a survey
Shuang Zhang, Rui Fan, Yuti Liu, et al.
Bioinformatics Advances (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 94
Automating the design-build-test-learn cycle towards next-generation bacterial cell factories
Nicolás Gurdo, Daniel C. Volke, Douglas McCloskey, et al.
New Biotechnology (2023) Vol. 74, pp. 1-15
Open Access | Times Cited: 44
Deep generative design of RNA family sequences
Shunsuke Sumi, Michiaki Hamada, Hirohide Saito
Nature Methods (2024) Vol. 21, Iss. 3, pp. 435-443
Closed Access | Times Cited: 27
iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
Zhen Chen, Xuhan Liu, Pei Zhao, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. W1, pp. W434-W447
Open Access | Times Cited: 58
Prediction of RNA–protein interactions using a nucleotide language model
Keisuke Yamada, Michiaki Hamada
Bioinformatics Advances (2022) Vol. 2, Iss. 1
Open Access | Times Cited: 42
Integrated convolution and self-attention for improving peptide toxicity prediction
Shihu Jiao, Xiucai Ye, Tetsuya Sakurai, et al.
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 9
Caps-ac4C: an effective computational framework for identifying N4-acetylcytidine sites in human mRNA based on deep learning
Lantian Yao, Peilin Xie, Danhong Dong, et al.
Journal of Molecular Biology (2025), pp. 168961-168961
Closed Access | Times Cited: 1
Computational tools for aptamer identification and optimization
Di Sun, Miao Sun, Jialü Zhang, et al.
TrAC Trends in Analytical Chemistry (2022) Vol. 157, pp. 116767-116767
Closed Access | Times Cited: 37
MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction
Honglei Wang, Tao Huang, Dong Wang, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 7
Seq-InSite: sequence supersedes structure for protein interaction site prediction
SeyedMohsen Hosseini, G. Brian Golding, Lucian Ilie
Bioinformatics (2024) Vol. 40, Iss. 1
Open Access | Times Cited: 6
Bioinformatics approaches for unveiling virus-host interactions
Hitoshi Iuchi, Junna Kawasaki, Kento Kubo, et al.
Computational and Structural Biotechnology Journal (2023) Vol. 21, pp. 1774-1784
Open Access | Times Cited: 13
An introduction to representation learning for single-cell data analysis
Ihuan Gunawan, Fatemeh Vafaee, Erik Meijering, et al.
Cell Reports Methods (2023) Vol. 3, Iss. 8, pp. 100547-100547
Open Access | Times Cited: 12
Improved prediction of MHC-peptide binding using protein language models
Nasser Hashemi, Boran Hao, Mikhail Ignatov, et al.
Frontiers in Bioinformatics (2023) Vol. 3
Open Access | Times Cited: 11
Improving language model of human genome for DNA–protein binding prediction based on task-specific pre-training
Hanyu Luo, Wenyu Shan, Cheng Chen, et al.
Interdisciplinary Sciences Computational Life Sciences (2022)
Closed Access | Times Cited: 17
Short human eccDNAs are predictable from sequences
Kai-Li Chang, Jiahong Chen, Tzu‐Chieh Lin, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 10
Deciphering genomic codes using advanced natural language processing techniques: a scoping review
Shuyan Cheng, Yishu Wei, Yiliang Zhou, et al.
Journal of the American Medical Informatics Association (2025)
Closed Access
A Review on the Applications of Transformer-based language models for Nucleotide Sequence Analysis
Nimisha Ghosh, Daniele Santoni, Indrajit Saha, et al.
Computational and Structural Biotechnology Journal (2025)
Open Access
DPCIPI: A pre-trained deep learning model for predicting cross-immunity between drifted strains of Influenza A/H3N2
Yiming Du, Zhuotian Li, Qian He, et al.
Journal of Automation and Intelligence (2025)
Open Access
Modeling microbiome-trait associations with taxonomy-adaptive neural networks
Yifan Jiang, Matthew Aton, Qiyun Zhu, et al.
Microbiome (2025) Vol. 13, Iss. 1
Open Access
Pato: prediction of probiotic bacteria using metabolic features
Rafaella Sinnott Dias, Daniela Peres Martinez, Fábio Pereira Leivas Leite, et al.
Brazilian Journal of Microbiology (2025)
Closed Access
Protein Representations: Encoding Biological Information for Machine Learning in Biocatalysis
David Harding-Larsen, Jonathan Funk, Niklas Gesmar Madsen, et al.
(2024)
Open Access | Times Cited: 3
CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network
Lantian Yao, Peilin Xie, Jiahui Guan, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 14, pp. 5725-5736
Open Access | Times Cited: 3
Protein representations: Encoding biological information for machine learning in biocatalysis
David Harding-Larsen, Jonathan Funk, Niklas Gesmar Madsen, et al.
Biotechnology Advances (2024) Vol. 77, pp. 108459-108459
Closed Access | Times Cited: 3
Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape
Andreas Wagner
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 2
Page 1 - Next Page

Scroll to top