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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:

iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences
Wei Chen, Pengmian Feng, Hui Yang, et al.
Oncotarget (2016) Vol. 8, Iss. 3, pp. 4208-4217
Open Access | Times Cited: 229

Showing 26-50 of 229 citing articles:

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins
Xuan Xiao, Xiang Cheng, Shengchao Su, et al.
Natural Science (2017) Vol. 09, Iss. 09, pp. 330-349
Open Access | Times Cited: 133
iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC
Yaser Daanial Khan, Nouman Rasool, Waqar Hussain, et al.
Analytical Biochemistry (2018) Vol. 550, pp. 109-116
Closed Access | Times Cited: 128
pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC
Xiang Cheng, Wei‐Zhong Lin, Xuan Xiao, et al.
Bioinformatics (2018) Vol. 35, Iss. 3, pp. 398-406
Open Access | Times Cited: 125
A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization
Wuritu Yang, Xiaojuan Zhu, Jian Huang, et al.
Current Bioinformatics (2018) Vol. 14, Iss. 3, pp. 234-240
Closed Access | Times Cited: 125
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
Waqar Hussain, Yaser Daanial Khan, Nouman Rasool, et al.
Analytical Biochemistry (2018) Vol. 568, pp. 14-23
Closed Access | Times Cited: 122
iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC
Bin Liu, Fan Weng, De-Shuang Huang, et al.
Bioinformatics (2018) Vol. 34, Iss. 18, pp. 3086-3093
Open Access | Times Cited: 121
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
pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC
Xuan Xiao, Xiang Cheng, Gen-Qiang Chen, et al.
Genomics (2018) Vol. 111, Iss. 4, pp. 886-892
Open Access | Times Cited: 115
iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition
Muhammad Arif, Maqsood Hayat, Zahoor Jan
Journal of Theoretical Biology (2018) Vol. 442, pp. 11-21
Closed Access | Times Cited: 108
A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides
Lei Xu, Guangmin Liang, Longjie Wang, et al.
Genes (2018) Vol. 9, Iss. 3, pp. 158-158
Open Access | Times Cited: 108
Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC
M. Fazli Sabooh, Nadeem Iqbal, Mukhtaj Khan, et al.
Journal of Theoretical Biology (2018) Vol. 452, pp. 1-9
Closed Access | Times Cited: 107
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
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
iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features
Shahana Yasmin Chowdhury, Swakkhar Shatabda, Abdollah Dehzangi
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 96
iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition
Muhammad Awais, Waqar Hussain, Yaser Daanial Khan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019) Vol. 18, Iss. 2, pp. 596-610
Closed Access | Times Cited: 95
DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest
Balachandran Manavalan, Tae Hwan Shin, Gwang Lee
Oncotarget (2017) Vol. 9, Iss. 2, pp. 1944-1956
Open Access | Times Cited: 93
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
iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule
Muhammad Tahir, Hilal Tayara, Kil To Chong
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 189, pp. 96-101
Closed Access | Times Cited: 91
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
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
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
IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction
Qi Zhao, Yue Zhang, Huan Hu, et al.
Frontiers in Genetics (2018) Vol. 9
Open Access | Times Cited: 84
pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Journal of Theoretical Biology (2018) Vol. 458, pp. 92-102
Closed Access | Times Cited: 84

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