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:

Machine learning techniques for protein function prediction
Rosalin Bonetta, Gianluca Valentino
Proteins Structure Function and Bioinformatics (2019) Vol. 88, Iss. 3, pp. 397-413
Closed Access | Times Cited: 132

Showing 1-25 of 132 citing articles:

Current progress and open challenges for applying deep learning across the biosciences
Nicolae Sapoval, Amirali Aghazadeh, Michael Nute, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 218
iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Zhen Chen, Pei Zhao, Chen Li, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 10, pp. e60-e60
Open Access | Times Cited: 197
A roadmap for metagenomic enzyme discovery
Serina L. Robinson, Jörn Piel, Shinichi Sunagawa
Natural Product Reports (2021) Vol. 38, Iss. 11, pp. 1994-2023
Open Access | Times Cited: 119
Biological Sequence Classification: A Review on Data and General Methods
Chunyan Ao, Shihu Jiao, Yansu Wang, et al.
Research (2022) Vol. 2022
Open Access | Times Cited: 70
A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions
Sneha Murmu, Dipro Sinha, Himanshushekhar Chaurasia, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 19
Biosystems Design by Machine Learning
Michael Volk, Ismini Lourentzou, Shekhar Mishra, et al.
ACS Synthetic Biology (2020) Vol. 9, Iss. 7, pp. 1514-1533
Closed Access | Times Cited: 105
Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function
Amelia Villegas-Morcillo, Stavros Makrodimitris, Roeland C. H. J. van Ham, et al.
Bioinformatics (2020) Vol. 37, Iss. 2, pp. 162-170
Open Access | Times Cited: 105
Machine learning-assisted enzyme engineering
Niklas E. Siedhoff, Ulrich Schwaneberg, Mehdi D. Davari
Methods in enzymology on CD-ROM/Methods in enzymology (2020), pp. 281-315
Closed Access | Times Cited: 89
Machine learning in bioprocess development: from promise to practice
Laura M. Helleckes, Johannes Hemmerich, Wolfgang Wiechert, et al.
Trends in biotechnology (2022) Vol. 41, Iss. 6, pp. 817-835
Open Access | Times Cited: 58
Unlocking the potential of enzyme engineering via rational computational design strategies
Lei Zhou, Chunmeng Tao, Xiaolin Shen, et al.
Biotechnology Advances (2024) Vol. 73, pp. 108376-108376
Closed Access | Times Cited: 15
Precision enzyme discovery through targeted mining of metagenomic data
Shohreh Ariaeenejad, Javad Gharechahi, Mehdi Foroozandeh Shahraki, et al.
Natural Products and Bioprospecting (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications
Xin Zhang, Zakir Ibrahim, Muhammad Bilawal Khaskheli, et al.
Sustainability (2024) Vol. 16, Iss. 17, pp. 7651-7651
Open Access | Times Cited: 10
Extremozymes: Challenges and opportunities on the road to novel enzymes production
Diego I.J. Salas-Bruggink, Jorge Sánchez-San Martín, Gabriel Leiva, et al.
Process Biochemistry (2024) Vol. 143, pp. 323-336
Closed Access | Times Cited: 9
Prediction of Thermodynamic Properties of C60-Based Fullerenols Using Machine Learning
Guiping Yang, Shu Zhang, Zhao Pei, et al.
Journal of Chemical Theory and Computation (2025)
Closed Access | Times Cited: 1
Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review
Jiaying Chen, Jingfu Wang, Yue Hu, et al.
Frontiers in Bioengineering and Biotechnology (2025) Vol. 13
Open Access | Times Cited: 1
IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides
Kaveh Kavousi, Mojtaba Bagheri, Saman Behrouzi, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 10, pp. 4691-4701
Closed Access | Times Cited: 69
A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction
Richa Dhanuka, Jyoti Prakash Singh, Anushree Tripathi
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023) Vol. 20, Iss. 3, pp. 2291-2301
Closed Access | Times Cited: 19
Machine learning for beam dynamics studies at the CERN Large Hadron Collider
Pasquale Arpaïa, Gabriella Azzopardi, F. Blanc, et al.
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (2020) Vol. 985, pp. 164652-164652
Open Access | Times Cited: 40
Discovery and mining of enzymes from the human gut microbiome
Baolei Jia, Xiao Han, Kyung Hyun Kim, et al.
Trends in biotechnology (2021) Vol. 40, Iss. 2, pp. 240-254
Closed Access | Times Cited: 37
TEMPROT: protein function annotation using transformers embeddings and homology search
Gabriel B. Oliveira, Hélio Pedrini, Zanoni Dias
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 14
Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor
Ailan Huang, Fuping Lu, Fufeng Liu
Frontiers in Microbiology (2023) Vol. 14
Open Access | Times Cited: 13
EpitopeVec: linear epitope prediction using deep protein sequence embeddings
Akash Bahai, Ehsaneddin Asgari, Mohammad R. K. Mofrad, et al.
Bioinformatics (2021) Vol. 37, Iss. 23, pp. 4517-4525
Open Access | Times Cited: 28
Computational methods for protein localization prediction
Yuexu Jiang, Duolin Wang, Weiwei Wang, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 5834-5844
Open Access | Times Cited: 28
Predicting plant Rubisco kinetics from RbcL sequence data using machine learning
Wasim A Iqbal, Alexei Lisitsa, Maxim V. Kapralov
Journal of Experimental Botany (2022) Vol. 74, Iss. 2, pp. 638-650
Open Access | Times Cited: 20
Domain-PFP allows protein function prediction using function-aware domain embedding representations
Nabil Ibtehaz, Yuki Kagaya, Daisuke Kihara
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 12
Page 1 - Next Page

Scroll to top