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:

iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition
Chang-Jian Zhang, Hua Tang, Wenchao Li, et al.
Oncotarget (2016) Vol. 7, Iss. 43, pp. 69783-69793
Open Access | Times Cited: 175

Showing 1-25 of 175 citing articles:

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

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

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

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

iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators
Chao-Qin Feng, Zhao‐Yue Zhang, Xiaojuan Zhu, et al.
Bioinformatics (2018) Vol. 35, Iss. 9, pp. 1469-1477
Closed Access | Times Cited: 206

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
Jiangning Song, Yanan Wang, Fuyi Li, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 2, pp. 638-658
Open Access | Times Cited: 204

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
Wang‐Ren Qiu, Shiyu Jiang, Zhaochun Xu, et al.
Oncotarget (2017) Vol. 8, Iss. 25, pp. 41178-41188
Open Access | Times Cited: 198

CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency
Leyi Wei, Pengwei Xing, Ran Su, et al.
Journal of Proteome Research (2017) Vol. 16, Iss. 5, pp. 2044-2053
Closed Access | Times Cited: 193

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

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network
Renzhi Cao, Colton Freitas, Leong Chan, et al.
Molecules (2017) Vol. 22, Iss. 10, pp. 1732-1732
Open Access | Times Cited: 185

Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique
Leyi Wei, Pengwei Xing, Gaotao Shi, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017) Vol. 16, Iss. 4, pp. 1264-1273
Closed Access | Times Cited: 180

Identify origin of replication inSaccharomyces cerevisiaeusing two-step feature selection technique
Fanny Dao, Hao Lv, Fang Wang, et al.
Bioinformatics (2018) Vol. 35, Iss. 12, pp. 2075-2083
Closed Access | Times Cited: 180

Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition
Hao Lin, Zhi-Yong Liang, Hua Tang, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017) Vol. 16, Iss. 4, pp. 1316-1321
Closed Access | Times Cited: 172

HBPred: a tool to identify growth hormone-binding proteins
Hua Tang, Ya-Wei Zhao, Ping Zou, et al.
International Journal of Biological Sciences (2018) Vol. 14, Iss. 8, pp. 957-964
Open Access | Times Cited: 172

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

4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction
Wenying He, Cangzhi Jia, Quan Zou
Bioinformatics (2018) Vol. 35, Iss. 4, pp. 593-601
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

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction
Yumeng Liu, Xiaolong Wang, Bin Liu
Briefings in Bioinformatics (2017) Vol. 20, Iss. 1, pp. 330-346
Closed Access | Times Cited: 154

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

Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites
Leyi Wei, Ran Su, Bing Wang, et al.
Neurocomputing (2018) Vol. 324, pp. 3-9
Closed Access | Times Cited: 137

Page 1 - Next Page

Scroll to top