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26 results on '"Xie, Haoling"'

9. Supplementary Table from Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

Author

Wang, Yicheng, primary, Xie, Haoling, primary, Chang, Xiaohong, primary, Hu, Wenqi, primary, Li, Mengyao, primary, Li, Yi, primary, Liu, Huiping, primary, Cheng, Hongyan, primary, Wang, Shang, primary, Zhou, Ling, primary, Shen, Danhua, primary, Dou, Sha, primary, Ma, Ruiqiong, primary, Mao, Yunuo, primary, Zhu, Honglan, primary, Zhang, Xiaobo, primary, Zheng, Yuxuan, primary, Ye, Xue, primary, Wen, Lu, primary, Kee, Kehkooi, primary, Cui, Heng, primary, and Tang, Fuchou, primary

Published
2023
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10. Supplementary Figure from Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

Author

Wang, Yicheng, primary, Xie, Haoling, primary, Chang, Xiaohong, primary, Hu, Wenqi, primary, Li, Mengyao, primary, Li, Yi, primary, Liu, Huiping, primary, Cheng, Hongyan, primary, Wang, Shang, primary, Zhou, Ling, primary, Shen, Danhua, primary, Dou, Sha, primary, Ma, Ruiqiong, primary, Mao, Yunuo, primary, Zhu, Honglan, primary, Zhang, Xiaobo, primary, Zheng, Yuxuan, primary, Ye, Xue, primary, Wen, Lu, primary, Kee, Kehkooi, primary, Cui, Heng, primary, and Tang, Fuchou, primary

Published
2023
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11. Supplementary Data from Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

Author

Wang, Yicheng, primary, Xie, Haoling, primary, Chang, Xiaohong, primary, Hu, Wenqi, primary, Li, Mengyao, primary, Li, Yi, primary, Liu, Huiping, primary, Cheng, Hongyan, primary, Wang, Shang, primary, Zhou, Ling, primary, Shen, Danhua, primary, Dou, Sha, primary, Ma, Ruiqiong, primary, Mao, Yunuo, primary, Zhu, Honglan, primary, Zhang, Xiaobo, primary, Zheng, Yuxuan, primary, Ye, Xue, primary, Wen, Lu, primary, Kee, Kehkooi, primary, Cui, Heng, primary, and Tang, Fuchou, primary

Published
2023
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12. Data from Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

Author

Wang, Yicheng, primary, Xie, Haoling, primary, Chang, Xiaohong, primary, Hu, Wenqi, primary, Li, Mengyao, primary, Li, Yi, primary, Liu, Huiping, primary, Cheng, Hongyan, primary, Wang, Shang, primary, Zhou, Ling, primary, Shen, Danhua, primary, Dou, Sha, primary, Ma, Ruiqiong, primary, Mao, Yunuo, primary, Zhu, Honglan, primary, Zhang, Xiaobo, primary, Zheng, Yuxuan, primary, Ye, Xue, primary, Wen, Lu, primary, Kee, Kehkooi, primary, Cui, Heng, primary, and Tang, Fuchou, primary

Published
2023
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14. Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

Author

Wang, Yicheng, primary, Xie, Haoling, additional, Chang, Xiaohong, additional, Hu, Wenqi, additional, Li, Mengyao, additional, Li, Yi, additional, Liu, Huiping, additional, Cheng, Hongyan, additional, Wang, Shang, additional, Zhou, Ling, additional, Shen, Danhua, additional, Dou, Sha, additional, Ma, Ruiqiong, additional, Mao, Yunuo, additional, Zhu, Honglan, additional, Zhang, Xiaobo, additional, Zheng, Yuxuan, additional, Ye, Xue, additional, Wen, Lu, additional, Kee, Kehkooi, additional, Cui, Heng, additional, and Tang, Fuchou, additional

Published
2022
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20. Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features.

Author

Dong, Ji, Zhou, Peijie, Wu, Yichong, Chen, Yidong, Xie, Haoling, Gao, Yuan, Lu, Jiansen, Yang, Jingwei, Zhang, Xiannian, Wen, Lu, Li, Tiejun, and Tang, Fuchou

Subjects
DATA integration, RNA sequencing, PROTEIN-protein interactions, SCALABILITY, WORKFLOW
Abstract

With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration. [ABSTRACT FROM AUTHOR]

Published
2022
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21. Enhancing single-cell cellular state inference by incorporating molecular network features

Author

Dong, Ji, primary, Zhou, Peijie, additional, Wu, Yichong, additional, Wang, Wendong, additional, Chen, Yidong, additional, Zhou, Xin, additional, Xie, Haoling, additional, Gao, Yuan, additional, Lu, Jiansen, additional, Yang, Jingwei, additional, Zhang, Xiannian, additional, Wen, Lu, additional, Fu, Wei, additional, Li, Tiejun, additional, and Tang, Fuchou, additional

Published
2019
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25. Underwater Image Restoration Based on Background Light Estimation and Dark Channel Prior

Author

王凡 Wang Fan, 彭国华 Peng Guohua, 谢昊伶 Xie Haoling, and 杨成 Yang Cheng

Subjects
Channel (digital image), Computer science, business.industry, 02 engineering and technology, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Underwater, Background light, business, Image restoration
Published
2018
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26. LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning.

Author

Yang, Cheng, Yang, Longshu, Zhou, Man, Zhang, Chengjiu, Xie, Haoling, Zhu, Huaiqiu, and Wang, May D

Subjects
NON-coding RNA, DEEP learning, GENOMIC imprinting, CELL differentiation, MESSENGER RNA, RNA sequencing
Abstract

Motivation To characterize long non-coding RNAs (lncRNAs), both identifying and functionally annotating them are essential to be addressed. Moreover, a comprehensive construction for lncRNA annotation is desired to facilitate the research in the field. Results We present LncADeep, a novel lncRNA identification and functional annotation tool. For lncRNA identification, LncADeep integrates intrinsic and homology features into a deep belief network and constructs models targeting both full- and partial-length transcripts. For functional annotation, LncADeep predicts a lncRNA's interacting proteins based on deep neural networks, using both sequence and structure information. Furthermore, LncADeep integrates KEGG and Reactome pathway enrichment analysis and functional module detection with the predicted interacting proteins, and provides the enriched pathways and functional modules as functional annotations for lncRNAs. Test results show that LncADeep outperforms state-of-the-art tools, both for lncRNA identification and lncRNA–protein interaction prediction, and then presents a functional interpretation. We expect that LncADeep can contribute to identifying and annotating novel lncRNAs. Availability and implementation LncADeep is freely available for academic use at http://cqb.pku.edu.cn/ZhuLab/lncadeep/ and https://github.com/cyang235/LncADeep/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2018
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