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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:

iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals
Xiang Cheng, Shuguang Zhao, Xuan Xiao, et al.
Bioinformatics (2016) Vol. 33, Iss. 3, pp. 341-346
Open Access | Times Cited: 217

Showing 1-25 of 217 citing articles:

Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC
Prabina Kumar Meher, Tanmaya Kumar Sahu, Varsha Saini, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 453
iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Bin Liu, Fan Yang, De-Shuang Huang, et al.
Bioinformatics (2017) Vol. 34, Iss. 1, pp. 33-40
Open Access | Times Cited: 318
iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
Pengmian Feng, Hui Yang, Hui Ding, et al.
Genomics (2018) Vol. 111, Iss. 1, pp. 96-102
Open Access | Times Cited: 298
iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC
Pengmian Feng, Hui Ding, Hui Yang, et al.
Molecular Therapy — Nucleic Acids (2017) Vol. 7, pp. 155-163
Open Access | Times Cited: 285
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC
Zhendong Su, Yan Huang, Zhao‐Yue Zhang, et al.
Bioinformatics (2018) Vol. 34, Iss. 24, pp. 4196-4204
Open Access | Times Cited: 273
MLACP: machine-learning-based prediction of anticancer peptides
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Oncotarget (2017) Vol. 8, Iss. 44, pp. 77121-77136
Open Access | Times Cited: 242
2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function
Bin Liu, Fan Yang, Kuo‐Chen Chou
Molecular Therapy — Nucleic Acids (2017) Vol. 7, pp. 267-277
Open Access | Times Cited: 241
LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
Cheng Chen, Qingmei Zhang, Qin Ma, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 191, pp. 54-64
Open Access | Times Cited: 232
pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Genomics (2017) Vol. 110, Iss. 1, pp. 50-58
Open Access | Times Cited: 228
Behavioral Modeling for Mental Health using Machine Learning Algorithms
M S Srividya, S. Mohanavalli, N. Bhalaji
Journal of Medical Systems (2018) Vol. 42, Iss. 5
Closed Access | Times Cited: 208
pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Molecular BioSystems (2017) Vol. 13, Iss. 9, pp. 1722-1727
Closed Access | Times Cited: 188
iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition
Wei Chen, Hui Ding, Xu Zhou, et al.
Analytical Biochemistry (2018) Vol. 561-562, pp. 59-65
Closed Access | Times Cited: 187
pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites
Xiang Cheng, Shuguang Zhao, Wei‐Zhong Lin, et al.
Bioinformatics (2017) Vol. 33, Iss. 22, pp. 3524-3531
Open Access | Times Cited: 183
pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Genomics (2017) Vol. 110, Iss. 4, pp. 231-239
Open Access | Times Cited: 165
iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Genomics (2017) Vol. 110, Iss. 5, pp. 239-246
Closed Access | Times Cited: 160
pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Gene (2017) Vol. 628, pp. 315-321
Closed Access | Times Cited: 159
pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Bioinformatics (2017) Vol. 34, Iss. 9, pp. 1448-1456
Open Access | Times Cited: 159
Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC
Yinan Shen, Jijun Tang, Fei Guo
Journal of Theoretical Biology (2018) Vol. 462, pp. 230-239
Closed Access | Times Cited: 153
A similarity-based method for prediction of drug side effects with heterogeneous information
Xian Zhao, Lei Chen, Jing Lu
Mathematical Biosciences (2018) Vol. 306, pp. 136-144
Closed Access | Times Cited: 151
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
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
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