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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:

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine
Pengwei Xing, Ran Su, Fei Guo, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA
Quan Zou, Pengwei Xing, Leyi Wei, et al.
RNA (2018) Vol. 25, Iss. 2, pp. 205-218
Open Access | Times Cited: 463
ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides
Leyi Wei, Chen Zhou, Huangrong Chen, et al.
Bioinformatics (2018) Vol. 34, Iss. 23, pp. 4007-4016
Open Access | Times Cited: 385
Prediction of human protein subcellular localization using deep learning
Leyi Wei, Yijie Ding, Ran Su, et al.
Journal of Parallel and Distributed Computing (2017) Vol. 117, pp. 212-217
Closed Access | Times Cited: 208
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
CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency
Leyi Wei, Pengwei Xing, Ran Su, et al.
Journal of Proteome Research (2017) Vol. 16, Iss. 5, pp. 2044-2053
Closed Access | Times Cited: 192
iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition
Wei Chen, Hui Ding, Xu Zhou, et al.
Analytical Biochemistry (2018) Vol. 561-562, pp. 59-65
Closed Access | Times Cited: 186
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
Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites
Leyi Wei, Ran Su, Bing Wang, et al.
Neurocomputing (2018) Vol. 324, pp. 3-9
Closed Access | Times Cited: 137
SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides
Leyi Wei, Jijun Tang, Quan Zou
BMC Genomics (2017) Vol. 18, Iss. S7
Open Access | Times Cited: 123
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
BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach
Yu Huang, Ningning He, Yu Chen, et al.
International Journal of Biological Sciences (2018) Vol. 14, Iss. 12, pp. 1669-1677
Open Access | Times Cited: 96
A Novel Modeling in Mathematical Biology for Classification of Signal Peptides
Asma Ehsan, Khalid Mahmood, Yaser Daanial Khan, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 84
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
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
Prediction of bio-sequence modifications and the associations with diseases
Chunyan Ao, Liang Yu, Quan Zou
Briefings in Functional Genomics (2020) Vol. 20, Iss. 1, pp. 1-18
Closed Access | Times Cited: 72
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism
Lesong Wei, Xiucai Ye, Yuyang Xue, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 72
Umami-MRNN: Deep learning-based prediction of umami peptide using RNN and MLP
Lulu Qi, Jialuo Du, Yue Sun, et al.
Food Chemistry (2022) Vol. 405, pp. 134935-134935
Closed Access | Times Cited: 38
Recent advances in the plant epitranscriptome
Lisha Shen, Jinqi Ma, Ping Li, et al.
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 32
Simulation screening of umami peptides in the microbiota of soy sauce based on molecular docking and molecular dynamics
Qi Wu, Chunming Xu, Yuan Tian, et al.
Food Bioscience (2024) Vol. 61, pp. 104722-104722
Closed Access | Times Cited: 8
RNA methylation and diseases: experimental results, databases, Web servers and computational models
Xing Chen, Yazhou Sun, Hui Liu, et al.
Briefings in Bioinformatics (2017) Vol. 20, Iss. 3, pp. 896-917
Closed Access | Times Cited: 85
KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides
Poonam Pandey, Vinal Patel, Nithin V. George, et al.
Journal of Proteome Research (2018) Vol. 17, Iss. 9, pp. 3214-3222
Closed Access | Times Cited: 75
RFAthM6A: a new tool for predicting m6A sites in Arabidopsis thaliana
Xiaofeng Wang, Renxiang Yan
Plant Molecular Biology (2018) Vol. 96, Iss. 3, pp. 327-337
Closed Access | Times Cited: 67
Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection
José Liñares-Blanco, Ana B. Porto-Pazos, Alejandro Pazos, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 63
A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae
Xiaolei Zhu, Jingjing He, Shihao Zhao, et al.
Briefings in Functional Genomics (2019)
Closed Access | Times Cited: 60
Imbalance learning for the prediction of N6-Methylation sites in mRNAs
Zhixun Zhao, Hui Peng, Chaowang Lan, et al.
BMC Genomics (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 59
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