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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:

im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network
Kewei Liu, Lei Cao, Pu-Feng Du, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 21, pp. 1044-1049
Open Access | Times Cited: 47

Showing 26-50 of 47 citing articles:

ELMo4m6A: A Contextual Language Embedding-Based Predictor for Detecting RNA N6-Methyladenosine Sites
Yongxian Fan, Guicong Sun, Xiaoyong Pan
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 2, pp. 944-954
Closed Access | Times Cited: 8
Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning
Zhengtao Luo, Liliang Lou, Wang‐Ren Qiu, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 24, pp. 15490-15490
Open Access | Times Cited: 8
BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities
Mingzhao Wang, Haider Ali, Yandi Xu, et al.
Journal of Biological Chemistry (2024) Vol. 300, Iss. 4, pp. 107140-107140
Open Access | Times Cited: 1
Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction
Jie Chen, Huilian Zhang, Quan Zou, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 3, pp. 755-768
Closed Access | Times Cited: 1
MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts
Qiaosen Su, Le Thi Phan, Nhat Truong Pham, et al.
Journal of Molecular Biology (2024), pp. 168856-168856
Closed Access | Times Cited: 1
predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance
Sabit Ahmed, Afrida Rahman, Md. Al Mehedi Hasan, et al.
PLoS ONE (2021) Vol. 16, Iss. 4, pp. e0249396-e0249396
Open Access | Times Cited: 10
Advances in detecting N6-methyladenosine modification in circRNAs
Lixia Ma, Lina He, Shiyang Kang, et al.
Methods (2022) Vol. 205, pp. 234-246
Closed Access | Times Cited: 7
Dynamic regulation and key roles of ribonucleic acid methylation
Jia Zou, Hui Liu, Wei Tan, et al.
Frontiers in Cellular Neuroscience (2022) Vol. 16
Open Access | Times Cited: 7
Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network
Wenxing Hu, Lixin Guan, Mengshan Li
PLoS Computational Biology (2023) Vol. 19, Iss. 8, pp. e1011370-e1011370
Open Access | Times Cited: 3
Tissue specific prediction of N6-methyladenine sites based on an ensemble of multi-input hybrid neural network
Cangzhi Jia, Dong Jin, Xin Wang, et al.
Biocell (2021) Vol. 46, Iss. 4, pp. 1105-1121
Open Access | Times Cited: 7
Tissue-specific RNA methylation prediction from gene expression data using sparse regression models
Jie Jiang, Bowen Song, Jia Meng, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107892-107892
Closed Access | Times Cited: 2
StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble
Yaqun Zhang, Zhaomin Yu, Bin Yu, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 222, pp. 104495-104495
Open Access | Times Cited: 4
LPI-SKMSC: Predicting LncRNA–Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering
Dian-Zheng Sun, Zhan-Li Sun, Mengya Liu, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 2, pp. 378-391
Closed Access
Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification
Huilian Zhang, Jie Chen, Bo Liao, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 2, pp. 455-468
Closed Access
GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species
Qi Zhang, Yuxiao Wei, Liwei Liu
Computers in Biology and Medicine (2024) Vol. 180, pp. 108974-108974
Closed Access
Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites
Zhengtao Luo, Liyi Yu, Zhaochun Xu, et al.
Biology (2024) Vol. 13, Iss. 10, pp. 777-777
Open Access
Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites
Nguyen Quoc Khanh Le, Xu Ling
ACS Omega (2023) Vol. 8, Iss. 42, pp. 39420-39426
Open Access | Times Cited: 1
im5C-DSCGA: A Proposed Hybrid Framework Based on Improved DenseNet and Attention Mechanisms for Identifying 5-methylcytosine Sites in Human RNA
Jianhua Jia, Lulu Qin, Rufeng Lei
Frontiers in Bioscience-Landmark (2023) Vol. 28, Iss. 12, pp. 346-346
Open Access | Times Cited: 1
DNA/RNA sequence feature representation algorithms for predicting methylation-modified sites
Juanying Xie, Mingzhao Wang, Sheng‐Quan Xu
Scientia Sinica Vitae (2022) Vol. 53, Iss. 6, pp. 841-875
Closed Access | Times Cited: 2
Classifying the superfamily of small heat shock proteins by using g-gap dipeptide compositions
Pengmian Feng, Weiwei Liu, Cong Huang, et al.
International Journal of Biological Macromolecules (2020) Vol. 167, pp. 1575-1578
Closed Access | Times Cited: 2
A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression
Binhua Tang, Yuqi Wang, Yu Chen, et al.
Frontiers in Cell and Developmental Biology (2021) Vol. 8
Open Access | Times Cited: 2
Learning RNA sequence patterns to interpretably identify m6A modification sites
Guodong Li, Bo-Wei Zhao, Xiaorui Su, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2023), pp. 1248-1253
Closed Access
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