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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:

iPTM-mLys: identifying multiple lysine PTM sites and their different types
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Bioinformatics (2016) Vol. 32, Iss. 20, pp. 3116-3123
Open Access | Times Cited: 254

Showing 26-50 of 254 citing articles:

DPP-PseAAC: A DNA-binding protein prediction model using Chou’s general PseAAC
Mohammad Saifur Rahman, Swakkhar Shatabda, Sanjay Saha, et al.
Journal of Theoretical Biology (2018) Vol. 452, pp. 22-34
Closed Access | Times Cited: 149
iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
Nguyen Quoc Khanh Le, Edward Kien Yee Yapp, Quang‐Thai Ho, et al.
Analytical Biochemistry (2019) Vol. 571, pp. 53-61
Closed Access | Times Cited: 141
iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
Shahid Akbar, Maqsood Hayat
Journal of Theoretical Biology (2018) Vol. 455, pp. 205-211
Closed 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
SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
Waqar Hussain, Yaser Daanial Khan, Nouman Rasool, et al.
Journal of Theoretical Biology (2019) Vol. 468, pp. 1-11
Closed Access | Times Cited: 136
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
O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique
Cangzhi Jia, Yun Zuo, Quan Zou
Bioinformatics (2018) Vol. 34, Iss. 12, pp. 2029-2036
Open Access | Times Cited: 127
Large-scale comparative assessment of computational predictors for lysine post-translational modification sites
Zhen Chen, Xuhan Liu, Fuyi Li, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 6, pp. 2267-2290
Open Access | Times Cited: 126
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
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
GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences
Wankun Deng, Chenwei Wang, Ying Zhang, et al.
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 118
PseUI: Pseudouridine sites identification based on RNA sequence information
Jingjing He, Ting Fang, Zizheng Zhang, et al.
BMC Bioinformatics (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 116
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
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
Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC
Muslim Khan, Maqsood Hayat, Sher Afzal Khan, et al.
Journal of Theoretical Biology (2016) Vol. 415, pp. 13-19
Closed Access | Times Cited: 101
iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components
Muhammad Tahir, Hilal Tayara, Kil To Chong
Journal of Theoretical Biology (2018) Vol. 465, pp. 1-6
Open Access | Times Cited: 101
iATC-NRAKEL: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs
Jianpeng Zhou, Lei Chen, Zihan Guo
Bioinformatics (2019) Vol. 36, Iss. 5, pp. 1391-1396
Open Access | Times Cited: 101
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
Sequence-based predictive modeling to identify cancerlectins
Hong-Yan Lai, Xinxin Chen, Wei Chen, et al.
Oncotarget (2017) Vol. 8, Iss. 17, pp. 28169-28175
Open Access | Times Cited: 96
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

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