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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 for inferring transcription factor binding sites
Peter K. Koo, Matt Ploenzke
Current Opinion in Systems Biology (2020) Vol. 19, pp. 16-23
Open Access | Times Cited: 67

Showing 1-25 of 67 citing articles:

JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles
Jaime A. Castro-Mondragón, Rafael Riudavets Puig, Ieva Rauluševičiūtė, et al.
Nucleic Acids Research (2021) Vol. 50, Iss. D1, pp. D165-D173
Open Access | Times Cited: 1505
Obtaining genetics insights from deep learning via explainable artificial intelligence
Gherman Novakovsky, Nick Dexter, Maxwell W. Libbrecht, et al.
Nature Reviews Genetics (2022) Vol. 24, Iss. 2, pp. 125-137
Closed Access | Times Cited: 228
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks
Peter K. Koo, Antonio Majdandzic, Matthew Ploenzke, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 5, pp. e1008925-e1008925
Open Access | Times Cited: 70
Improving representations of genomic sequence motifs in convolutional networks with exponential activations
Peter K. Koo, Matt Ploenzke
Nature Machine Intelligence (2021) Vol. 3, Iss. 3, pp. 258-266
Open Access | Times Cited: 66
Transcription factor-based biosensors for screening and dynamic regulation
Jonathan Tellechea‐Luzardo, Martin T. Stiebritz, Pablo Carbonell
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access | Times Cited: 36
ExplaiNN: interpretable and transparent neural networks for genomics
Gherman Novakovsky, Oriol Fornés, Manu Saraswat, et al.
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 24
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Emrullah Şahin, Naciye Nur Arslan, Durmuş Özdemir
Neural Computing and Applications (2024)
Closed Access | Times Cited: 11
Enhancer–silencer transitions in the human genome
Di Huang, Ivan Ovcharenko
Genome Research (2022) Vol. 32, Iss. 3, pp. 437-448
Open Access | Times Cited: 29
EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations
Nicholas Keone Lee, Ziqi Tang, Shushan Toneyan, et al.
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 21
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models
E. Seitz, David M. McCandlish, Justin B. Kinney, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 6, pp. 701-713
Closed Access | Times Cited: 7
Biologically relevant transfer learning improves transcription factor binding prediction
Gherman Novakovsky, Manu Saraswat, Oriol Fornés, et al.
Genome biology (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 35
DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
Sandro Barissi, Alba Sala, Miłosz Wieczór, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. 16, pp. 9105-9114
Open Access | Times Cited: 23
TFBMiner: A User-Friendly Command Line Tool for the Rapid Mining of Transcription Factor-Based Biosensors
Erik K. R. Hanko, Tariq A. Joosab Noor Mahomed, Ruth Stoney, et al.
ACS Synthetic Biology (2023) Vol. 12, Iss. 5, pp. 1497-1507
Open Access | Times Cited: 15
A survey on algorithms to characterize transcription factor binding sites
Manuel Tognon, Rosalba Giugno, Luca Pinello
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Open Access | Times Cited: 14
Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors
Min Li, Zhenya Chen, Yi‐Xin Huo
ACS Sensors (2024) Vol. 9, Iss. 10, pp. 5002-5024
Closed Access | Times Cited: 6
Learning the Regulatory Code of Gene Expression
Jan Zrimec, Filip Buric, Mariia Kokina, et al.
Frontiers in Molecular Biosciences (2021) Vol. 8
Open Access | Times Cited: 29
Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation
Andrew Duncan, Jennifer A. Mitchell, Alan M Moses
Bioinformatics (2024) Vol. 40, Iss. 4
Open Access | Times Cited: 4
Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion
Anirban Sarkar, Ziqi Tang, Chris Zhao, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
A cost‐effective tsCUT&Tag method for profiling transcription factor binding landscape
Leiming Wu, Zi Wei Luo, Yanni Shi, et al.
Journal of Integrative Plant Biology (2022) Vol. 64, Iss. 11, pp. 2033-2038
Closed Access | Times Cited: 19
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection
Andrea Di Gioacchino, Jonah Procyk, Marco Molari, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 9, pp. e1010561-e1010561
Open Access | Times Cited: 18
Towards a better understanding of TF-DNA binding prediction from genomic features
Zixuan Wang, Meiqin Gong, Yuhang Liu, et al.
Computers in Biology and Medicine (2022) Vol. 149, pp. 105993-105993
Closed Access | Times Cited: 17
DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences
Yao Zhou, Wenjing Zhang, Peng Song, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 11
ProPr54 web server: predicting σ54 promoters and regulon with a hybrid convolutional and recurrent deep neural network
Tristan Achterberg, Anne de Jong
NAR Genomics and Bioinformatics (2025) Vol. 7, Iss. 1
Open Access
An Efficient Deep Learning Approach for DNA-Binding Proteins Classification from Primary Sequences
Nosiba Yousif Ahmed, Wafa Alameen Alsanousi, Eman Mohammed Hamid, et al.
International Journal of Computational Intelligence Systems (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 3
Evaluating deep learning for predicting epigenomic profiles
Shushan Toneyan, Ziqi Tang, Peter K. Koo
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 14
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