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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:

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

Showing 1-25 of 206 citing articles:

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Medicinal Research Reviews (2020) Vol. 40, Iss. 4, pp. 1276-1314
Closed Access | Times Cited: 256
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
i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome
Wei Chen, Hao Lv, Fulei Nie, et al.
Bioinformatics (2019) Vol. 35, Iss. 16, pp. 2796-2800
Closed Access | Times Cited: 196
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
iProEP: A Computational Predictor for Predicting Promoter
Hong-Yan Lai, Zhao‐Yue Zhang, Zhendong Su, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 17, pp. 337-346
Open Access | Times Cited: 153
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 131-141
Open Access | Times Cited: 151
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
Evaluation of different computational methods on 5-methylcytosine sites identification
Hao Lv, Zimei Zhang, Shi-Hao Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 982-995
Closed 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 hormone binding proteins based on machine learning methods
Jiu-Xin Tan, Shi-Hao Li, Zimei Zhang, et al.
Mathematical Biosciences & Engineering (2019) Vol. 16, Iss. 4, pp. 2466-2480
Open Access | Times Cited: 135
Design powerful predictor for mRNA subcellular location prediction inHomo sapiens
Zhao‐Yue Zhang, Yuhe R. Yang, Hui Ding, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 1, pp. 526-535
Open Access | Times Cited: 125
iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides
Phasit Charoenkwan, Janchai Yana, Chanin Nantasenamat, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 6666-6678
Closed Access | Times Cited: 121
MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
Meng Zhang, Fuyi Li, Tatiana T. Marquez‐Lago, et al.
Bioinformatics (2019) Vol. 35, Iss. 17, pp. 2957-2965
Open Access | Times Cited: 120
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
Leyi Wei, Wenjia He, Adeel Malik, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 114
Computational Methods for Identifying Similar Diseases
Liang Cheng, Hengqiang Zhao, Pingping Wang, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 590-604
Open Access | Times Cited: 113
iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2018) Vol. 16, pp. 412-420
Open Access | Times Cited: 111
A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features
Zhibin Lv, Shunshan Jin, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2019) Vol. 7
Open Access | Times Cited: 107
Biosystems Design by Machine Learning
Michael Volk, Ismini Lourentzou, Shekhar Mishra, et al.
ACS Synthetic Biology (2020) Vol. 9, Iss. 7, pp. 1514-1533
Closed Access | Times Cited: 107
Predicting Thermophilic Proteins by Machine Learning
Xianfang Wang, Peng Gao, Yifeng Liu, et al.
Current Bioinformatics (2020) Vol. 15, Iss. 5, pp. 493-502
Closed Access | Times Cited: 99
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
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 17, pp. 972-981
Open Access | Times Cited: 95
iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features
Wei Chen, Pengmian Feng, Xiaoming Song, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 269-274
Open Access | Times Cited: 95
iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule
Muhammad Tahir, Hilal Tayara, Kil To Chong
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 189, pp. 96-101
Closed Access | Times Cited: 91
iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks
Muhammad Tahir, Hilal Tayara, Kil To Chong
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 463-470
Open Access | Times Cited: 85
Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs
Ping Xuan, Chang Sun, Tiangang Zhang, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 82
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