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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:

DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks
Ahmet Süreyya Rifaioğlu, Tunca Doğan, María Martin, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 138

Showing 1-25 of 138 citing articles:

Functional Performance of Plant Proteins
Kai Kai, Maija Greis, Jiakai Lu, et al.
Foods (2022) Vol. 11, Iss. 4, pp. 594-594
Open Access | Times Cited: 198
Learning functional properties of proteins with language models
Serbülent Ünsal, Heval Ataş, Muammer Albayrak, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 3, pp. 227-245
Closed Access | Times Cited: 134
ProteInfer, deep neural networks for protein functional inference
Theo Sanderson, Maxwell L. Bileschi, David Belanger, et al.
eLife (2023) Vol. 12
Open Access | Times Cited: 109
cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model
Shahid Akbar, Maqsood Hayat, Muhammad Tahir, et al.
Artificial Intelligence in Medicine (2022) Vol. 131, pp. 102349-102349
Closed Access | Times Cited: 95
AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks
Ali Raza, Jamal Uddin, Abdullah Almuhaimeed, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 21, pp. 6537-6554
Closed Access | Times Cited: 73
AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding
Lingyan Zheng, Shuiyang Shi, Mingkun Lu, et al.
Genome biology (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 36
Protein function prediction as approximate semantic entailment
Maxat Kulmanov, Francisco J. Guzmán‐Vega, Paula Duek, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 2, pp. 220-228
Open Access | Times Cited: 21
pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning
Muhammad Khalil Shahid, Maqsood Hayat, Wajdi Alghamdi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 3
Semantic similarity and machine learning with ontologies
Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Open Access | Times Cited: 130
Deep Learning in Proteomics
Bo Wen, Wen‐Feng Zeng, Yuxing Liao, et al.
PROTEOMICS (2020) Vol. 20, Iss. 21-22
Open Access | Times Cited: 128
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
Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications
Anna V. Leopold, Daria M. Shcherbakova, Vladislav V. Verkhusha
Frontiers in Cellular Neuroscience (2019) Vol. 13
Open Access | Times Cited: 104
TALE: Transformer-based protein function Annotation with joint sequence–Label Embedding
Yue Cao, Yang Shen
Bioinformatics (2021) Vol. 37, Iss. 18, pp. 2825-2833
Open Access | Times Cited: 96
iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach
Shahid Akbar, Salman Khan, Farman Ali, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 204, pp. 104103-104103
Closed Access | Times Cited: 89
MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
Ahmet Süreyya Rifaioğlu, Rengül Çetin-Atalay, Deniz Kahraman, et al.
Bioinformatics (2020) Vol. 37, Iss. 5, pp. 693-704
Closed Access | Times Cited: 85
Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks
Ashfaq Ahmad, Shahid Akbar, Salman Khan, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 208, pp. 104214-104214
Closed Access | Times Cited: 85
Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products
Sin Yong Teng, Guo Yong Yew, Kateřina Sukačová, et al.
Biotechnology Advances (2020) Vol. 44, pp. 107631-107631
Closed Access | Times Cited: 84
AI applications in functional genomics
Claudia Caudai, Antonella Galizia, Filippo Geraci, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 5762-5790
Open Access | Times Cited: 77
SDN2GO: An Integrated Deep Learning Model for Protein Function Prediction
Yideng Cai, Jiacheng Wang, Lei Deng
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 70
PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods
Weiqi Xia, Lingyan Zheng, Jiebin Fang, et al.
Computers in Biology and Medicine (2022) Vol. 145, pp. 105465-105465
Closed Access | Times Cited: 59
PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships
Tong Pan, Chen Li, Yue Bi, et al.
Bioinformatics (2023) Vol. 39, Iss. 3
Open Access | Times Cited: 23
Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method
Mary Maranga, Paweł Szczerbiak, Valentyn Bezshapkin, et al.
mSystems (2023) Vol. 8, Iss. 2
Open Access | Times Cited: 23
Machine Learning in Soft Matter: From Simulations to Experiments
Kaihua Zhang, Xiangrui Gong, Ying Jiang
Advanced Functional Materials (2024) Vol. 34, Iss. 24
Closed Access | Times Cited: 9
Deep learning for mining protein data
Qiang Shi, Weiya Chen, Siqi Huang, et al.
Briefings in Bioinformatics (2019) Vol. 22, Iss. 1, pp. 194-218
Closed Access | Times Cited: 67
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