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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:

A pitfall for machine learning methods aiming to predict across cell types
Jacob Schreiber, Ritambhara Singh, Jeffrey A. Bilmes, et al.
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

A guide to machine learning for biologists
Joe G. Greener, Shaun M. Kandathil, Lewis Moffat, et al.
Nature Reviews Molecular Cell Biology (2021) Vol. 23, Iss. 1, pp. 40-55
Open Access | Times Cited: 1277
Navigating the pitfalls of applying machine learning in genomics
Sean Whalen, Jacob Schreiber, William Stafford Noble, et al.
Nature Reviews Genetics (2021) Vol. 23, Iss. 3, pp. 169-181
Closed Access | Times Cited: 195
Essential guidelines for computational method benchmarking
Lukas M. Weber, Wouter Saelens, Robrecht Cannoodt, et al.
Genome biology (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 148
Prediction of histone post-translational modification patterns based on nascent transcription data
Zhong Wang, Alexandra G. Chivu, Lauren A. Choate, et al.
Nature Genetics (2022) Vol. 54, Iss. 3, pp. 295-305
Open Access | Times Cited: 81
A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods
Jill E. Moore, Henry Pratt, Michael Purcaro, et al.
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 99
ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination
Quan Xu, Γεώργιος Γεωργίου, Siebren Frölich, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 14, pp. 7966-7985
Open Access | Times Cited: 62
Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts
Surag Nair, Daniel Sunwook Kim, Jacob Perricone, et al.
Bioinformatics (2019) Vol. 35, Iss. 14, pp. i108-i116
Open Access | Times Cited: 69
Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples
Jacob Schreiber, Jeffrey A. Bilmes, William Stafford Noble
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 41
A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome
Zhenhao Zhang, Fan Feng, Yiyang Qiu, et al.
Nucleic Acids Research (2023) Vol. 51, Iss. 12, pp. 5931-5947
Open Access | Times Cited: 16
Predicting A/B compartments from histone modifications using deep learning
Suchen Zheng, Nitya Thakkar, Hannah L. Harris, et al.
iScience (2024) Vol. 27, Iss. 5, pp. 109570-109570
Open Access | Times Cited: 5
Predicting gene expression state and prioritizing putative enhancers using 5hmC signal
Edahí González‐Avalos, Atsushi Onodera, Daniela Samaniego‐Castruita, et al.
Genome biology (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 5
Machine and Deep Learning Methods for Predicting 3D Genome Organization
Brydon P. G. Wall, My Nguyen, J. Chuck Harrell, et al.
Methods in molecular biology (2024), pp. 357-400
Open Access | Times Cited: 4
The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles
Jacob Schreiber, Carles Boix, Jin wook Lee, et al.
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 11
Iterative improvement of deep learning models using synthetic regulatory genomics
André M. Ribeiro-dos-Santos, Matthew T. Maurano
bioRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access
Generative modeling for RNA splicing predictions and design
Di Wu, Natalie Maus, Anupama Jha, et al.
(2025)
Open Access
Generative modeling for RNA splicing predictions and design
Di Wu, Natalie Maus, Anupama Jha, et al.
(2025)
Open Access
Capturing large genomic contexts for accurately predicting enhancer-promoter interactions
Ken Chen, Huiying Zhao, Yuedong Yang
Briefings in Bioinformatics (2021) Vol. 23, Iss. 2
Open Access | Times Cited: 23
Identification of chromatin loops from Hi-C interaction matrices by CTCF–CTCF topology classification
Silvia Galan, François Serra, Marc A. Martı́-Renom
NAR Genomics and Bioinformatics (2022) Vol. 4, Iss. 1
Open Access | Times Cited: 15
Predicting cell type-specific epigenomic profiles accounting for distal genetic effects
Alan E Murphy, William Beardall, Marek Rei, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Closed Access | Times Cited: 3
Computational approaches to understand transcription regulation in development
Maarten van der Sande, Siebren Frölich, Simon J. van Heeringen
Biochemical Society Transactions (2023) Vol. 51, Iss. 1, pp. 1-12
Open Access | Times Cited: 7
Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network
Ali Yousefian-Jazi, Min Kyung Sung, Taeyeop Lee, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 17
Machine learning for profile prediction in genomics
Jacob Schreiber, Ritambhara Singh
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 35-41
Open Access | Times Cited: 16
Predicting A/B compartments from histone modifications using deep learning
Suchen Zheng, Nitya Thakkar, Hannah L. Harris, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 11
Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning
Ben Lai, Sheng Qian, Hanwei Zhang, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 5, pp. e1010011-e1010011
Open Access | Times Cited: 10
Toward Identification of Functional Sequences and Variants in Noncoding DNA
Remo Monti, Uwe Ohler
Annual Review of Biomedical Data Science (2023) Vol. 6, Iss. 1, pp. 191-210
Open Access | Times Cited: 5
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