Open Access Helper

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:

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

Showing 26-50 of 151 citing articles:

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches
Jiawei Wang, Bingjiao Yang, Yi An, et al.
Briefings in Bioinformatics (2017) Vol. 20, Iss. 3, pp. 931-951
Open Access | Times Cited: 72
Mounting Behaviour Recognition for Pigs Based on Deep Learning
Dan Li, Yifei Chen, Kaifeng Zhang, et al.
Sensors (2019) Vol. 19, Iss. 22, pp. 4924-4924
Open Access | Times Cited: 61
A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome
Zhibin Lv, Hui Ding, Lei Wang, et al.
Neurocomputing (2020) Vol. 422, pp. 214-221
Closed Access | Times Cited: 60
A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD
Zhiyu Tao, Yanjuan Li, Zhixia Teng, et al.
Computational and Mathematical Methods in Medicine (2020) Vol. 2020, pp. 1-9
Open Access | Times Cited: 57
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome
Shaherin Basith, Md Mehedi Hasan, Gwang Lee, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 54
Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species
Zutan Li, Hangjin Jiang, Lingpeng Kong, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 2, pp. e1008767-e1008767
Open Access | Times Cited: 45
Towards a better prediction of subcellular location of long non-coding RNA
Zhao‐Yue Zhang, Zi‐Jie Sun, Yuhe Yang, et al.
Frontiers of Computer Science (2022) Vol. 16, Iss. 5
Closed Access | Times Cited: 35
i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites
Mobeen Ur Rehman, Hilal Tayara, Quan Zou, et al.
Bioinformatics (2022) Vol. 38, Iss. 16, pp. 3885-3891
Open Access | Times Cited: 33
Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species
Xingyu Tang, Peijie Zheng, Xueyong Li, et al.
Methods (2022) Vol. 204, pp. 142-150
Closed Access | Times Cited: 29
Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach
Nhat Truong Pham, Le Thi Phan, Ji-Min Seo, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 20
ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information
Shaherin Basith, Nhat Truong Pham, Minkyung Song, et al.
Computers in Biology and Medicine (2023) Vol. 165, pp. 107386-107386
Closed Access | Times Cited: 17
Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions
Yixiao Zhai, Yu Chen, Zhixia Teng, et al.
Frontiers in Cell and Developmental Biology (2020) Vol. 8
Open Access | Times Cited: 49
6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes
Haodong Xu, Ruifeng Hu, Peilin Jia, et al.
Bioinformatics (2020) Vol. 36, Iss. 10, pp. 3257-3259
Open Access | Times Cited: 46
Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools
Balachandran Manavalan, Md Mehedi Hasan, Shaherin Basith, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 406-420
Open Access | Times Cited: 44
Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method
Zimei Zhang, Jia-Shu Wang, Hasan Zulfiqar, et al.
Frontiers in Cell and Developmental Biology (2020) Vol. 8
Open Access | Times Cited: 42
SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes
Zeeshan Abbas, Hilal Tayara, Kil To Chong
IEEE Access (2020) Vol. 8, pp. 201450-201457
Open Access | Times Cited: 41
Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes
Nicholas Dominic, Tjeng Wawan Cenggoro, Bens Pardamean
Procedia Computer Science (2023) Vol. 227, pp. 1054-1061
Open Access | Times Cited: 13
HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach
Nhat Truong Pham, Ying Zhang, Rajan Rakkiyappan, et al.
Computers in Biology and Medicine (2024) Vol. 179, pp. 108859-108859
Closed Access | Times Cited: 5
Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues
Guohua Huang, Xiaohong Huang, Jinyun Jiang
Methods (2024) Vol. 226, pp. 1-8
Closed Access | Times Cited: 4
High-Accuracy Identification and Structure–Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry
Wanxing Li, Xuejing Liu, Yuanfa Liu, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus
Shaherin Basith, Balachandran Manavalan, Gwang Lee
Methods (2025)
Closed Access
REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset
Le Thi Phan, Rajan Rakkiyappan, Balachandran Manavalan
Computers in Biology and Medicine (2025) Vol. 187, pp. 109771-109771
Closed Access
i6mA-stack: A stacking ensemble-based computational prediction of DNA N6-methyladenine (6mA) sites in the Rosaceae genome
Jhabindra Khanal, Dae Young Lim, Hilal Tayara, et al.
Genomics (2020) Vol. 113, Iss. 1, pp. 582-592
Open Access | Times Cited: 38
Critical evaluation of web-based DNA N6-methyladenine site prediction tools
Md Mehedi Hasan, Watshara Shoombuatong, Hiroyuki Kurata, et al.
Briefings in Functional Genomics (2020) Vol. 20, Iss. 4, pp. 258-272
Closed Access | Times Cited: 37
DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information
Saeed Ahmed, Muhammad Kabir, Muhammad Arif, et al.
Analytical Biochemistry (2020) Vol. 612, pp. 113955-113955
Closed Access | Times Cited: 35

Scroll to top