Výsledky vyhledávání - "Long-Chen Shen"

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level

Autor: Yan Liu, Guo Wei, Chen Li, Long-Chen Shen, Robin B Gasser, Jiangning Song, Dijun Chen, Dong-Jun Yu

Publikováno v: Briefings in Bioinformatics. 24

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::00c09abcdfb202fa40494531a20fce02
https://doi.org/10.1093/bib/bbad132

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Learning Cell Annotation under Multiple Reference Datasets by Multisource Domain Adaptation

Autor: Yan Liu, He Yan, Long-Chen Shen, Dong-Jun Yu

Publikováno v: Journal of chemical information and modeling. 63(1)

Accurate and efficient cell type annotation is essential for single-cell sequence analysis. Currently, cell type annotation using well-annotated reference datasets with powerful models has become increasingly popular. However, with the increasing amo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa5bda0f5ec56ee42e182d50b0e4fc3a
https://pubmed.ncbi.nlm.nih.gov/36579851

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Improving the prediction of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding

Autor: Yu-Hang Yin, Long-Chen Shen, Yuanhao Jiang, Shang Gao, Jiangning Song, Dong-Jun Yu

Publikováno v: Analytical biochemistry. 656

Accurate prediction of DNA-protein binding (DPB) is of great biological significance for studying the regulatory mechanism of gene expression. In recent years, with the rapid development of deep learning techniques, advanced deep neural networks have

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5c0ee80d8f48109264568327c124b3d
https://pubmed.ncbi.nlm.nih.gov/36049552

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MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network

Autor: Jiangning Song, Ke Han, Yi-Heng Zhu, Jian Xu, Dong-Jun Yu, Long-Chen Shen

Publikováno v: Brief Bioinform

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have bee

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9d39b524a222c0c1dd9dec4c3c7bff3
https://doi.org/10.1093/bib/bbab445

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Improving protein fold recognition using triplet network and ensemble deep learning

Autor: Dong-Jun Yu, Long-Chen Shen, Yan Liu, Yi-Heng Zhu, Jiangning Song, Ke Han, Ying Zhang

Publikováno v: Brief Bioinform

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive adva

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f76909782f84fd25cf02b8bce6be313
https://doi.org/10.1093/bib/bbab248

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SAResNet: self-attention residual network for predicting DNA-protein binding

Autor: Jiangning Song, Dong-Jun Yu, Long-Chen Shen, Yan Liu

Publikováno v: Brief Bioinform

Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bf0041d5a6f961e0cd888f2f9017b39
https://doi.org/10.1093/bib/bbab101

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