Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteineS-nitrosylation sites in proteins
Yan Xu, Xiaojian Shao, Ling‐Yun Wu, et al.
PeerJ (2013) Vol. 1, pp. e171-e171
Open Access | Times Cited: 272

Showing 51-75 of 272 citing articles:

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC
Hui Yang, Wang‐Ren Qiu, Guoqing Liu, et al.
International Journal of Biological Sciences (2018) Vol. 14, Iss. 8, pp. 883-891
Open Access | Times Cited: 142
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
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
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
iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples
Muhammad Kabir, Maqsood Hayat
Molecular Genetics and Genomics (2015) Vol. 291, Iss. 1, pp. 285-296
Closed Access | Times Cited: 133
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
A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
Jun Zhang, Bin Liu
Current Bioinformatics (2018) Vol. 14, Iss. 3, pp. 190-199
Closed Access | Times Cited: 132
iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition
Xuan Xiao, Han-Xiao Ye, Zi Liu, et al.
Oncotarget (2016) Vol. 7, Iss. 23, pp. 34180-34189
Open Access | Times Cited: 127
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
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
Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition
Loris Nanni, Sheryl Brahnam, Alessandra Lumini
Journal of Theoretical Biology (2014) Vol. 360, pp. 109-116
Closed Access | Times Cited: 119
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
PSNO: Predicting Cysteine S-Nitrosylation Sites by Incorporating Various Sequence-Derived Features into the General Form of Chou’s PseAAC
Jian Zhang, Xiaowei Zhao, Pingping Sun, et al.
International Journal of Molecular Sciences (2014) Vol. 15, Iss. 7, pp. 11204-11219
Open Access | Times Cited: 108
Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation
Ruifeng Xu, Jiyun Zhou, Hongpeng Wang, et al.
BMC Systems Biology (2015) Vol. 9, Iss. S1
Open Access | Times Cited: 106
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
Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition
Cangzhi Jia, Xin Lin, Zhiping Wang
International Journal of Molecular Sciences (2014) Vol. 15, Iss. 6, pp. 10410-10423
Open Access | Times Cited: 104
iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC
Muhammad Tahir, Maqsood Hayat
Molecular BioSystems (2016) Vol. 12, Iss. 8, pp. 2587-2593
Closed Access | Times Cited: 104
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
Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC
Zhe Ju, Junzhe Cao, Hong Gu
Journal of Theoretical Biology (2016) Vol. 397, pp. 145-150
Closed Access | Times Cited: 98
Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks
Yiheng Wang, Tong Liu, Dong Xu, et al.
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 97

Scroll to top