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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.

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Showing 13 citing articles:

Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more suitable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods
Y‐h. Taguchi, Turki Turki
Genomics (2023) Vol. 115, Iss. 2, pp. 110577-110577
Open Access | Times Cited: 8
Integrated Analysis of Gene Expression and Protein–Protein Interaction with Tensor Decomposition
Y‐h. Taguchi, Turki Turki
Mathematics (2023) Vol. 11, Iss. 17, pp. 3655-3655
Open Access | Times Cited: 4
Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction
Y‐h. Taguchi, Turki Turki
Frontiers in Artificial Intelligence (2023) Vol. 6
Open Access | Times Cited: 4
TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction
Y‐h. Taguchi, Turki Turki
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 3
Optimized Tensor Decomposition and Principal Component Analysis Outperforming State-of-the-Art Methods When Analyzing Histone Modification Chromatin Immunoprecipitation Profiles
Turki Turki, Sanjiban Sekhar Roy, Y‐h. Taguchi
Algorithms (2023) Vol. 16, Iss. 9, pp. 401-401
Open Access | Times Cited: 2
Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more suitable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods
Y‐h. Taguchi, Turki Turki
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 4
Tensor Decomposition and Principal Component Analysis-Based Unsupervised Feature Extraction Outperforms State-of-the-Art Methods When Applied to Histone Modification Profiles
Sanjiban Sekhar Roy, Y‐h. Taguchi
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 3
maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism
Turki Turki, Y‐h. Taguchi
Mathematics (2024) Vol. 12, Iss. 10, pp. 1536-1536
Open Access
Theoretical Investigation of TD- and PCA-Based Unsupervised FE
Y‐h. Taguchi
Unsupervised and semi-supervised learning (2024), pp. 449-503
Closed Access
Optimization methods for tensor decomposition: A comparison of new algorithms for fitting the CP(CANDECOMP/PARAFAC) model
Huiwen Yu, Kasper Green Larsen, Ove Christiansen
Chemometrics and Intelligent Laboratory Systems (2024), pp. 105290-105290
Open Access
Integrated analysis of gene expression and protein-protein interaction with tensor decomposition
Y‐h. Taguchi, Turki Turki
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism
Turki Turki, Y‐h. Taguchi
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
Tensor Decomposition-based Unsupervised Feature Extraction Succeeded in Identification of Differentially Expressed Transcripts from Redundantde novoTranscriptome ofPlanarian
Makoto Kashima, Nobuyoshi Kumagai, Hiromi Hirata, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access
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