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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:

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest
Balachandran Manavalan, Tae Hwan Shin, Gwang Lee
Oncotarget (2017) Vol. 9, Iss. 2, pp. 1944-1956
Open Access | Times Cited: 93

Showing 1-25 of 93 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
Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response
Ran Su, Xinyi Liu, Leyi Wei, et al.
Methods (2019) Vol. 166, pp. 91-102
Closed Access | Times Cited: 227
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Bioinformatics (2018) Vol. 35, Iss. 16, pp. 2757-2765
Closed Access | Times Cited: 225
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
AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest
Balachandran Manavalan, Tae Hwan Shin, Myeong O. Kim, et al.
Frontiers in Pharmacology (2018) Vol. 9
Open Access | Times Cited: 194
Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy
Balachandran Manavalan, Sathiyamoorthy Subramaniyam, Tae Hwan Shin, et al.
Journal of Proteome Research (2018) Vol. 17, Iss. 8, pp. 2715-2726
Closed Access | Times Cited: 192
Predicting protein structural classes for low-similarity sequences by evaluating different features
Xiaojuan Zhu, Chao-Qin Feng, Hong-Yan Lai, et al.
Knowledge-Based Systems (2018) Vol. 163, pp. 787-793
Closed Access | Times Cited: 192
PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine
Balachandran Manavalan, Tae Hwan Shin, Gwang Lee
Frontiers in Microbiology (2018) Vol. 9
Open Access | Times Cited: 182
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides
Vinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 8, pp. 1964-1964
Open Access | Times Cited: 168
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
iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction
Balachandran Manavalan, Rajiv Gandhi Govindaraj, Tae Hwan Shin, et al.
Frontiers in Immunology (2018) Vol. 9
Open Access | Times Cited: 161
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
iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites inHomo sapiens
Hui Yang, Hao Lv, Hui Ding, et al.
Journal of Computational Biology (2018) Vol. 25, Iss. 11, pp. 1266-1277
Open 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
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
Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework
Md Mehedi Hasan, Shaherin Basith, Mst. Shamima Khatun, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 114
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
PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
Balachandran Manavalan, Tae Hwan Shin, Myeong Ok Kim, et al.
Frontiers in Immunology (2018) Vol. 9
Open Access | Times Cited: 111
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
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Cells (2019) Vol. 8, Iss. 11, pp. 1332-1332
Open Access | Times Cited: 91
Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method
Zimei Zhang, Jiu-Xin Tan, Fang Wang, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 86
i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome
Md Mehedi Hasan, Balachandran Manavalan, Mst. Shamima Khatun, et al.
International Journal of Biological Macromolecules (2019) Vol. 157, pp. 752-758
Closed Access | Times Cited: 82
i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, et al.
Plant Molecular Biology (2020) Vol. 103, Iss. 1-2, pp. 225-234
Closed Access | Times Cited: 78
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