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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:

Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy
Bin Liu, Longyun Fang, Shanyi Wang, et al.
Journal of Theoretical Biology (2015) Vol. 385, pp. 153-159
Closed Access | Times Cited: 156

Showing 26-50 of 156 citing articles:

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Molecular Informatics (2016) Vol. 36, Iss. 5-6
Closed Access | Times Cited: 154
Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Journal of Biomolecular Structure and Dynamics (2015) Vol. 34, Iss. 9, pp. 1946-1961
Open Access | Times Cited: 140
iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals
Xiang Cheng, Shuguang Zhao, Xuan Xiao, et al.
Oncotarget (2017) Vol. 8, Iss. 35, pp. 58494-58503
Open Access | Times Cited: 136
PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition
Yongchun Zuo, Yuan Li, Chen Ying-li, et al.
Bioinformatics (2016) Vol. 33, Iss. 1, pp. 122-124
Open Access | Times Cited: 135
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: 124
Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods
Bin Liu, Hao Wu, Deyuan Zhang, et al.
Oncotarget (2017) Vol. 8, Iss. 8, pp. 13338-13343
Open Access | Times Cited: 120
Using deformation energy to analyze nucleosome positioning in genomes
Wei Chen, Pengmian Feng, Hui Ding, et al.
Genomics (2015) Vol. 107, Iss. 2-3, pp. 69-75
Closed Access | Times Cited: 116
iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
Jianhua Jia, Xiaoyan Li, Wang‐Ren Qiu, et al.
Journal of Theoretical Biology (2018) Vol. 460, pp. 195-203
Closed Access | Times Cited: 105
pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
Kuo‐Chen Chou, Xiang Cheng, Xuan Xiao
Genomics (2018) Vol. 111, Iss. 6, pp. 1274-1282
Open Access | Times Cited: 92
Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
Kuo‐Chen Chou
Current Medicinal Chemistry (2019) Vol. 26, Iss. 26, pp. 4918-4943
Closed Access | Times Cited: 91
iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
Xuan Xiao, Zhaochun Xu, Wang‐Ren Qiu, et al.
Genomics (2018) Vol. 111, Iss. 6, pp. 1785-1793
Open Access | Times Cited: 88
Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
Zhe Ju, Shiyun Wang
Genomics (2019) Vol. 112, Iss. 1, pp. 859-866
Closed Access | Times Cited: 85
70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features
Wenying He, Cangzhi Jia, Yucong Duan, et al.
BMC Systems Biology (2018) Vol. 12, Iss. S4
Open Access | Times Cited: 84
MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule
Xiuquan Du, Yanyu Diao, Heng Liu, et al.
Journal of Proteome Research (2019) Vol. 18, Iss. 8, pp. 3119-3132
Closed Access | Times Cited: 84
Progresses in Predicting Post-translational Modification
Kuo‐Chen Chou
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 2, pp. 873-888
Closed Access | Times Cited: 82
Protein fold recognition based on multi-view modeling
Ke Yan, Xiaozhao Fang, Yong Xu, et al.
Bioinformatics (2019) Vol. 35, Iss. 17, pp. 2982-2990
Closed Access | Times Cited: 81
Relapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinoma
Meng Zhou, Wanying Xu, Yue X, et al.
Oncotarget (2016) Vol. 7, Iss. 20, pp. 29720-29738
Open Access | Times Cited: 79
PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction
Abdollah Dehzangi, Yosvany López, Sunil Pranit Lal, et al.
Journal of Theoretical Biology (2017) Vol. 425, pp. 97-102
Closed Access | Times Cited: 74
Identifying anticancer peptides by using improved hybrid compositions
Feng‐Min Li, Xiaoqian Wang
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 71
RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule
Lei Zheng, Shenghui Huang, Nengjiang Mu, et al.
Database (2019) Vol. 2019
Open Access | Times Cited: 70
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
PACES: prediction of N4-acetylcytidine (ac4C) modification sites in mRNA
Wanqing Zhao, Yiran Zhou, Qinghua Cui, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 67
SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids
Yosvany López, Abdollah Dehzangi, Sunil Pranit Lal, et al.
Analytical Biochemistry (2017) Vol. 527, pp. 24-32
Closed Access | Times Cited: 66
Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams
Abdollah Dehzangi, Yosvany López, Sunil Pranit Lal, et al.
PLoS ONE (2018) Vol. 13, Iss. 2, pp. e0191900-e0191900
Open Access | Times Cited: 66
iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families
Muhammad Kabir, Saeed Ahmad, Muhammad Iqbal, et al.
Genomics (2019) Vol. 112, Iss. 1, pp. 276-285
Open Access | Times Cited: 66

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