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:
iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice
Hao Lv, Fanny Dao, Zheng-Xing Guan, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 71
Hao Lv, Fanny Dao, Zheng-Xing Guan, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 71
Showing 1-25 of 71 citing articles:
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: 113
Md Mehedi Hasan, Shaherin Basith, Mst. Shamima Khatun, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 113
iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes
Hao Lv, Fanny Dao, Dan Zhang, et al.
iScience (2020) Vol. 23, Iss. 4, pp. 100991-100991
Open Access | Times Cited: 104
Hao Lv, Fanny Dao, Dan Zhang, et al.
iScience (2020) Vol. 23, Iss. 4, pp. 100991-100991
Open Access | Times Cited: 104
Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
Nguyen Quoc Khanh Le, Quang‐Thai Ho
Methods (2021) Vol. 204, pp. 199-206
Closed Access | Times Cited: 81
Nguyen Quoc Khanh Le, Quang‐Thai Ho
Methods (2021) Vol. 204, pp. 199-206
Closed Access | Times Cited: 81
RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites
Zhibin Lv, Jun Zhang, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 79
Zhibin Lv, Jun Zhang, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 79
SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome
Haitao Yu, Zhiming Dai
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 78
Haitao Yu, Zhiming Dai
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 78
DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops
Fanny Dao, Hao Lv, Dan Zhang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 78
Fanny Dao, Hao Lv, Dan Zhang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 78
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: 77
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: 77
Prediction of bio-sequence modifications and the associations with diseases
Chunyan Ao, Liang Yu, Quan Zou
Briefings in Functional Genomics (2020) Vol. 20, Iss. 1, pp. 1-18
Closed Access | Times Cited: 72
Chunyan Ao, Liang Yu, Quan Zou
Briefings in Functional Genomics (2020) Vol. 20, Iss. 1, pp. 1-18
Closed Access | Times Cited: 72
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy
Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, et al.
Molecular Therapy (2022) Vol. 30, Iss. 8, pp. 2856-2867
Open Access | Times Cited: 66
Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, et al.
Molecular Therapy (2022) Vol. 30, Iss. 8, pp. 2856-2867
Open Access | Times Cited: 66
Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites
Ying Zhang, Yan Liu, Jian Xu, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 61
Ying Zhang, Yan Liu, Jian Xu, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 61
Machine learning: its challenges and opportunities in plant system biology
Mohsen Hesami, Milad Alizadeh, Andrew Maxwell Phineas Jones, et al.
Applied Microbiology and Biotechnology (2022) Vol. 106, Iss. 9-10, pp. 3507-3530
Closed Access | Times Cited: 50
Mohsen Hesami, Milad Alizadeh, Andrew Maxwell Phineas Jones, et al.
Applied Microbiology and Biotechnology (2022) Vol. 106, Iss. 9-10, pp. 3507-3530
Closed Access | Times Cited: 50
BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches
Sho Tsukiyama, Md Mehedi Hasan, Hong‐Wen Deng, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Open Access | Times Cited: 41
Sho Tsukiyama, Md Mehedi Hasan, Hong‐Wen Deng, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Open Access | Times Cited: 41
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
Zhibin Lv, Hui Ding, Lei Wang, et al.
Neurocomputing (2020) Vol. 422, pp. 214-221
Closed Access | Times Cited: 60
Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology
Zhibin Lv, Donghua Wang, Hui Ding, et al.
IEEE Access (2020) Vol. 8, pp. 14851-14859
Open Access | Times Cited: 53
Zhibin Lv, Donghua Wang, Hui Ding, et al.
IEEE Access (2020) Vol. 8, pp. 14851-14859
Open Access | Times Cited: 53
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: 47
Zutan Li, Hangjin Jiang, Lingpeng Kong, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 2, pp. e1008767-e1008767
Open Access | Times Cited: 47
iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA
Wei Su, Xue-Qin Xie, Xiaowei Liu, et al.
International Journal of Biological Macromolecules (2022) Vol. 227, pp. 1174-1181
Closed Access | Times Cited: 35
Wei Su, Xue-Qin Xie, Xiaowei Liu, et al.
International Journal of Biological Macromolecules (2022) Vol. 227, pp. 1174-1181
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
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
Xingyu Tang, Peijie Zheng, Xueyong Li, et al.
Methods (2022) Vol. 204, pp. 142-150
Closed Access | Times Cited: 29
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
Haodong Xu, Ruifeng Hu, Peilin Jia, et al.
Bioinformatics (2020) Vol. 36, Iss. 10, pp. 3257-3259
Open Access | Times Cited: 46
6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion
Qianfei Huang, Jun Zhang, Leyi Wei, et al.
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 45
Qianfei Huang, Jun Zhang, Leyi Wei, et al.
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 45
i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features
Liang Kong, Lichao Zhang
Genes (2019) Vol. 10, Iss. 10, pp. 828-828
Open Access | Times Cited: 44
Liang Kong, Lichao Zhang
Genes (2019) Vol. 10, Iss. 10, pp. 828-828
Open Access | Times Cited: 44
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
Zeeshan Abbas, Hilal Tayara, Kil To Chong
IEEE Access (2020) Vol. 8, pp. 201450-201457
Open Access | Times Cited: 41
A deep learning approach to automate whole‐genome prediction of diverse epigenomic modifications in plants
Yifan Wang, Pingxian Zhang, Weijun Guo, et al.
New Phytologist (2021) Vol. 232, Iss. 2, pp. 880-897
Open Access | Times Cited: 34
Yifan Wang, Pingxian Zhang, Weijun Guo, et al.
New Phytologist (2021) Vol. 232, Iss. 2, pp. 880-897
Open Access | Times Cited: 34
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
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
DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning
Abdul Wahab, Omid Mahmoudi, Jeehong Kim, et al.
Cells (2020) Vol. 9, Iss. 8, pp. 1756-1756
Open Access | Times Cited: 34
Abdul Wahab, Omid Mahmoudi, Jeehong Kim, et al.
Cells (2020) Vol. 9, Iss. 8, pp. 1756-1756
Open Access | Times Cited: 34
