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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:

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

Showing 1-25 of 121 citing articles:

Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 733-744
Open Access | Times Cited: 200
iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach
Bin Liu, Kai Li, De-Shuang Huang, et al.
Bioinformatics (2018) Vol. 34, Iss. 22, pp. 3835-3842
Open Access | Times Cited: 198
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
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
M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning
Leyi Wei, Huangrong Chen, Ran Su
Molecular Therapy — Nucleic Acids (2018) Vol. 12, pp. 635-644
Open Access | Times Cited: 170
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools
Ran Su, Jie Hu, Quan Zou, et al.
Briefings in Bioinformatics (2018) Vol. 21, Iss. 2, pp. 408-420
Closed Access | Times Cited: 148
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
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
Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms
Leyi Wei, Jie Hu, Fuyi Li, et al.
Briefings in Bioinformatics (2018)
Closed Access | Times Cited: 115
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
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
pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
Kuo‐Chen Chou, Xiang Cheng, Xuan Xiao
Genomics (2018) Vol. 111, Iss. 6, pp. 1274-1282
Open Access | Times Cited: 92
Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
Kuo‐Chen Chou
Current Medicinal Chemistry (2019) Vol. 26, Iss. 26, pp. 4918-4943
Closed Access | Times Cited: 91
iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
Xuan Xiao, Zhaochun Xu, Wang‐Ren Qiu, et al.
Genomics (2018) Vol. 111, Iss. 6, pp. 1785-1793
Open Access | Times Cited: 88
pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Journal of Theoretical Biology (2018) Vol. 458, pp. 92-102
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
A computational platform to identify origins of replication sites in eukaryotes
Fanny Dao, Hao Lv, Hasan Zulfiqar, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 2, pp. 1940-1950
Open Access | Times Cited: 82
Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC
Faisal Javed, Maqsood Hayat
Genomics (2018) Vol. 111, Iss. 6, pp. 1325-1332
Closed Access | Times Cited: 77
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
AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine
Chaolu Meng, Shunshan Jin, Lei Wang, et al.
Frontiers in Bioengineering and Biotechnology (2019) Vol. 7
Open Access | Times Cited: 70
iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC
Yaser Daanial Khan, Nouman Rasool, Waqar Hussain, et al.
Molecular Biology Reports (2018) Vol. 45, Iss. 6, pp. 2501-2509
Closed Access | Times Cited: 67
UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components
Xiaoqiang Cui, Zhaomin Yu, Bin Yu, et al.
Chemometrics and Intelligent Laboratory Systems (2018) Vol. 184, pp. 28-43
Open Access | Times Cited: 67
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
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
Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features
Duyen Thi, Nguyen Quoc Khanh Le
Genomics (2020) Vol. 112, Iss. 3, pp. 2445-2451
Open Access | Times Cited: 59
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