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12 results on '"Xiang, Dongxu"'

6. iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Author

Chen, Zhen, primary, Zhao, Pei, additional, Li, Chen, additional, Li, Fuyi, additional, Xiang, Dongxu, additional, Chen, Yong-Zi, additional, Akutsu, Tatsuya, additional, Daly, Roger J, additional, Webb, Geoffrey I, additional, Zhao, Quanzhi, additional, Kurgan, Lukasz, additional, and Song, Jiangning, additional

Published
2021
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7. Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Author

Mei, Shutao, primary, Li, Fuyi, additional, Xiang, Dongxu, additional, Ayala, Rochelle, additional, Faridi, Pouya, additional, Webb, Geoffrey I, additional, Illing, Patricia T, additional, Rossjohn, Jamie, additional, Akutsu, Tatsuya, additional, Croft, Nathan P, additional, Purcell, Anthony W, additional, and Song, Jiangning, additional

Published
2021
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10. Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.

Author

Zhu, Yan, Li, Fuyi, Xiang, Dongxu, Akutsu, Tatsuya, Song, Jiangning, and Jia, Cangzhi

Subjects
CAPSULE neural networks, CONVOLUTIONAL neural networks, DEEP learning, DNA sequencing, RNA polymerases, DROSOPHILA melanogaster
Abstract

A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core promoter is essential for our understanding of the transcriptional regulation of genes. As a complement to conventional experimental methods, computational techniques with easy-to-use platforms as essential bioinformatics tools can be effectively applied to annotate the functions and physiological roles of promoters. In this work, we propose a deep learning-based method termed Depicter (D eep l e arning for p red ic ting promo ter), for identifying three specific types of promoters, i.e. promoter sequences with the TATA-box (TATA model), promoter sequences without the TATA-box (non-TATA model), and indistinguishable promoters (TATA and non-TATA model). Depicter is developed based on an up-to-date, species-specific dataset which includes Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana promoters. A convolutional neural network coupled with capsule layers is proposed to train and optimize the prediction model of Depicter. Extensive benchmarking and independent tests demonstrate that Depicter achieves an improved predictive performance compared with several state-of-the-art methods. The webserver of Depicter is implemented and freely accessible at https://depicter.erc.monash.edu/. [ABSTRACT FROM AUTHOR]

Published
2021
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