Zobrazeno 1 - 10 of 56 pro vyhledávání: '"Qinhu Zhang"'
1
Akademický článek
Cross‐Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide‐Level Deep Neural Network
Autor: Qinhu Zhang, Siguo Wang, Zhipeng Li, Yijie Pan, De‐Shuang Huang
Publikováno v: Advanced Science, Vol 11, Iss 36, Pp n/a-n/a (2024)
Abstract Cross‐species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross‐species predictive performance being consistently worse than within‐species perf
Externí odkaz: https://doaj.org/article/e8e5949bd0dd41db8b4a321930fe9d74
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2
Akademický článek
iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.
Autor: Lin Yuan, Ling Zhao, Jinling Lai, Yufeng Jiang, Qinhu Zhang, Zhen Shen, Chun-Hou Zheng, De-Shuang Huang
Publikováno v: PLoS Computational Biology, Vol 20, Iss 8, p e1012399 (2024)
Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been
Externí odkaz: https://doaj.org/article/4ceb37f9b5fc4afab0f3925f42072fb6
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3
Akademický článek
scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data
Autor: Zhen-Hao Guo, Yan Wu, Siguo Wang, Qinhu Zhang, Jin-Ming Shi, Yan-Bin Wang, Zhan-Heng Chen
Publikováno v: BMC Bioinformatics, Vol 24, Iss 1, Pp 1-14 (2023)
Abstract Background The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or ar
Externí odkaz: https://doaj.org/article/4b1520acd296408fa75366a0387eae14
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4
Akademický článek
Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network
Autor: Zhen Shen, Wei Liu, ShuJun Zhao, QinHu Zhang, SiGuo Wang, Lin Yuan
Publikováno v: Frontiers in Genetics, Vol 14 (2023)
Introduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites. These methods predict whether the CircRNA seq
Externí odkaz: https://doaj.org/article/cf114b82a6454fcf9f410f737cb5deae
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5
Akademický článek
iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction.
Autor: Lin Yuan, Jiawang Zhao, Zhen Shen, Qinhu Zhang, Yushui Geng, Chun-Hou Zheng, De-Shuang Huang
Publikováno v: PLoS Computational Biology, Vol 19, Iss 8, p e1011344 (2023)
Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. Durin
Externí odkaz: https://doaj.org/article/f36814d0649b4d8b81db5917ca5b1070
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6
Akademický článek
Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks
Autor: Zhen Shen, Yan Ling Shao, Wei Liu, Qinhu Zhang, Lin Yuan
Publikováno v: BMC Genomics, Vol 23, Iss 1, Pp 1-12 (2022)
Abstract Background Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes
Externí odkaz: https://doaj.org/article/eba010ab804c4515b6236ac3e6923225
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7
Akademický článek
DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.
Autor: Siguo Wang, Qinhu Zhang, Ying He, Zhen Cui, Zhenghao Guo, Kyungsook Han, De-Shuang Huang
Publikováno v: PLoS Computational Biology, Vol 18, Iss 10, p e1010572 (2022)
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide conta
Externí odkaz: https://doaj.org/article/3bcafffea78c49c3879f32854d356c14
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8
Akademický článek
Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture
Autor: Siguo Wang, Qinhu Zhang, Zhen Shen, Ying He, Zhen-Heng Chen, Jianqiang Li, De-Shuang Huang
Publikováno v: Molecular Therapy: Nucleic Acids, Vol 24, Iss , Pp 154-163 (2021)
The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. Recent research has shown that the double helix structure of nucleotides plays an important role in improving the accuracy and interpretability
Externí odkaz: https://doaj.org/article/5613196c730b47b7815fd2001efb451c
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9
Akademický článek
Base-resolution prediction of transcription factor binding signals by a deep learning framework.
Autor: Qinhu Zhang, Ying He, Siguo Wang, Zhanheng Chen, Zhenhao Guo, Zhen Cui, Qi Liu, De-Shuang Huang
Publikováno v: PLoS Computational Biology, Vol 18, Iss 3, p e1009941 (2022)
Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framewo
Externí odkaz: https://doaj.org/article/c701d34466cd47dfb1aa625af0ecf558
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