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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:

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning
Leyi Wei, Huangrong Chen, Ran Su
Molecular Therapy — Nucleic Acids (2018) Vol. 12, pp. 635-644
Open Access | Times Cited: 168

Showing 1-25 of 168 citing articles:

Emerging role of tumor-related functional peptides encoded by lncRNA and circRNA
Pan Wu, Yongzhen Mo, Peng Miao, et al.
Molecular Cancer (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 462
Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
Haoyu Zhang, Quan Zou, Ying Ju, et al.
Current Bioinformatics (2022) Vol. 17, Iss. 5, pp. 473-482
Closed Access | Times Cited: 293
Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response
Ran Su, Xinyi Liu, Leyi Wei, et al.
Methods (2019) Vol. 166, pp. 91-102
Closed Access | Times Cited: 225
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Bioinformatics (2018) Vol. 35, Iss. 16, pp. 2757-2765
Closed Access | Times Cited: 221
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 733-744
Open Access | Times Cited: 199
WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach
Kunqi Chen, Zhen Wei, Qing Zhang, et al.
Nucleic Acids Research (2019) Vol. 47, Iss. 7, pp. e41-e41
Open Access | Times Cited: 197
Application of Machine Learning in Microbiology
Kaiyang Qu, Fei Guo, Xiangrong Liu, et al.
Frontiers in Microbiology (2019) Vol. 10
Open Access | Times Cited: 169
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides
Vinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 8, pp. 1964-1964
Open Access | Times Cited: 167
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 131-141
Open Access | Times Cited: 151
A Survey on ensemble learning under the era of deep learning
Yongquan Yang, Haijun Lv, Ning Chen
Artificial Intelligence Review (2022) Vol. 56, Iss. 6, pp. 5545-5589
Closed Access | Times Cited: 147
Iterative feature representations improve N4-methylcytosine site prediction
Leyi Wei, Ran Su, Shasha Luan, et al.
Bioinformatics (2019) Vol. 35, Iss. 23, pp. 4930-4937
Closed Access | Times Cited: 126
Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1676-1696
Closed Access | Times Cited: 121
iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2018) Vol. 16, pp. 412-420
Open Access | Times Cited: 111
M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species
Xiaoli Qiang, Huangrong Chen, Xiucai Ye, et al.
Frontiers in Genetics (2018) Vol. 9
Open Access | Times Cited: 104
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Lei Xu, Guangmin Liang, Changrui Liao, et al.
Molecules (2018) Vol. 23, Iss. 12, pp. 3140-3140
Open Access | Times Cited: 102
iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC
Iman Nazari, Muhammad Tahir, Hilal Tayara, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 193, pp. 103811-103811
Closed Access | Times Cited: 97
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 17, pp. 972-981
Open Access | Times Cited: 94
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Cells (2019) Vol. 8, Iss. 11, pp. 1332-1332
Open Access | Times Cited: 91
Computational identification of N6-methyladenosine sites in multiple tissues of mammals
Fanny Dao, Hao Lv, Yuhe R. Yang, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 1084-1091
Open Access | Times Cited: 84
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design
Hao Lv, Lei Shi, Joshua William Berkenpas, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 84
Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs
Ping Xuan, Chang Sun, Tiangang Zhang, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 80
Incorporating Distance-Based Top-n-gram and Random Forest To Identify Electron Transport Proteins
Xiaoqing Ru, Lihong Li, Quan Zou
Journal of Proteome Research (2019) Vol. 18, Iss. 7, pp. 2931-2939
Closed Access | Times Cited: 79
Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
Nguyen Quoc Khanh Le, Edward Kien Yee Yapp, N. Nagasundaram, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 17, pp. 1245-1254
Open Access | Times Cited: 79
Advances in the profiling of N6-methyladenosine (m6A) modifications
Hongxiang Zheng, Xiansheng Zhang, Na Sui
Biotechnology Advances (2020) Vol. 45, pp. 107656-107656
Open Access | Times Cited: 79
DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning
Juntao Chen, Quan Zou, Jing Li
Frontiers of Computer Science (2021) Vol. 16, Iss. 2
Closed Access | Times Cited: 79
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