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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 51-75 of 229 citing articles:

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
Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
Shengli Zhang, Xin Duan
Journal of Theoretical Biology (2017) Vol. 437, pp. 239-250
Closed Access | Times Cited: 87
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
Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition
Wenying Qiu, Shan Li, Xiaoqiang Cui, et al.
Journal of Theoretical Biology (2018) Vol. 450, pp. 86-103
Closed Access | Times Cited: 78
pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
Yaser Daanial Khan, Mehreen Jamil, Waqar Hussain, et al.
Journal of Theoretical Biology (2018) Vol. 463, pp. 47-55
Closed Access | Times Cited: 77
cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components
Shahid Akbar, Ateeq Ur Rahman, Maqsood Hayat, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 196, pp. 103912-103912
Closed Access | Times Cited: 76
Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia
Lei Cai, Tao Huang, Jingjing Su, et al.
Molecular Therapy — Nucleic Acids (2018) Vol. 12, pp. 433-442
Open Access | Times Cited: 72
iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties
Phasit Charoenkwan, Nalini Schaduangrat, Chanin Nantasenamat, et al.
International Journal of Molecular Sciences (2019) Vol. 21, Iss. 1, pp. 75-75
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
iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule
Nguyen Quoc Khanh Le, Edward Kien Yee Yapp, Yu‐Yen Ou, et al.
Analytical Biochemistry (2019) Vol. 575, pp. 17-26
Closed Access | Times Cited: 67
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
Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods
Xiuyun Zhai, Mingtong Chen, Wencong Lu
Computational Materials Science (2018) Vol. 151, pp. 41-48
Closed Access | Times Cited: 66
iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition
Muhammad Tahir, Maqsood Hayat, Sher Afzal Khan
Molecular Genetics and Genomics (2018) Vol. 294, Iss. 1, pp. 199-210
Closed Access | Times Cited: 66
IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types
Ya-Wei Zhao, Zhendong Su, Wuritu Yang, et al.
International Journal of Molecular Sciences (2017) Vol. 18, Iss. 9, pp. 1838-1838
Open Access | Times Cited: 65
iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule
Sarah Ilyas, Waqar Hussain, Adeel Ashraf, et al.
Current Genomics (2019) Vol. 20, Iss. 4, pp. 275-292
Open Access | Times Cited: 65
iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components
Omar Barukab, Yaser Daanial Khan, Sher Afzal Khan, et al.
Current Genomics (2019) Vol. 20, Iss. 4, pp. 306-320
Open Access | Times Cited: 65
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression
Qiang Su, Wencong Lu, Dongshu Du, et al.
Oncotarget (2017) Vol. 8, Iss. 30, pp. 49359-49369
Open Access | Times Cited: 64
iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components
Lichao Zhang, Liang Kong
Journal of Theoretical Biology (2018) Vol. 441, pp. 1-8
Closed Access | Times Cited: 62
Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC
Ahmad Hassan Butt, Nouman Rasool, Yaser Daanial Khan
Molecular Biology Reports (2018) Vol. 45, Iss. 6, pp. 2295-2306
Closed Access | Times Cited: 62
iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule
Sharaf J. Malebary, Muhammad Safi ur Rehman, Yaser Daanial Khan
PLoS ONE (2019) Vol. 14, Iss. 11, pp. e0223993-e0223993
Open Access | Times Cited: 62
Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC
Ernesto Contreras‐Torres
Journal of Theoretical Biology (2018) Vol. 454, pp. 139-145
Closed Access | Times Cited: 61
dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components
Ning Qiao, Zhiqiang Ma, Xiaowei Zhao
Journal of Theoretical Biology (2019) Vol. 470, pp. 43-49
Closed Access | Times Cited: 59
Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC
Saba Amanat, Adeel Ashraf, Waqar Hussain, et al.
Current Bioinformatics (2019) Vol. 15, Iss. 5, pp. 396-407
Closed Access | Times Cited: 55
ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species
Ruyi Chen, Fuyi Li, Xudong Guo, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Open Access | Times Cited: 17

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