Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Shengcheng Dong"'
Dependency of the Cancer-Specific Transcriptional Regulation Circuitry on the Promoter DNA Methylome
Autor:
Yu Liu, Yang Liu, Rongyao Huang, Wanlu Song, Jiawei Wang, Zhengtao Xiao, Shengcheng Dong, Yang Yang, Xuerui Yang
Publikováno v:
Cell Reports, Vol 26, Iss 12, Pp 3461-3474.e5 (2019)
Summary: Dynamic dysregulation of the promoter DNA methylome is a signature of cancer. However, comprehensive understandings about how the DNA methylome is incorporated in the transcriptional regulation circuitry and involved in regulating the gene e
Externí odkaz:
https://doaj.org/article/19a7c929fe0b47e1a17f16c516adb344
Autor:
Shengcheng Dong, Nanxiang Zhao, Emma Spragins, Meenakshi S. Kagda, Mingjie Li, Pedro Assis, Otto Jolanki, Yunhai Luo, J. Michael Cherry, Alan P. Boyle, Benjamin C. Hitz
Publikováno v:
Nature Genetics. 55:724-726
Data to reproduce analysesin the manuscript. GWAS predictions of TLand models.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26bbcf7e93c81f70fcafdd895284809e
Interpreting predictive machine learning models to derive biological knowledge is the ultimate goal of developing models in the era of genomic data exploding. Recently, sequence-based deep learning models have greatly outperformed other machine learn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b85e1ad1a73a6959c27e7dca36b3f12a
https://doi.org/10.1101/2023.01.23.525250
https://doi.org/10.1101/2023.01.23.525250
Publikováno v:
2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT).
Autor:
Shengcheng Dong, Nanxiang Zhao, Emma Spragins, Meenakshi S. Kagda, Mingjie Li, Pedro Assis, Otto Jolanki, Yunhai Luo, J Michael Cherry, Alan P Boyle, Benjamin C Hitz
Nearly 90% of the disease risk-associated variants identified from genome-wide association studies (GWAS) are in non-coding regions of the genome. The annotations obtained from analyzing functional genomics assays can provide additional information t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6e59f7f84418a4aef6b436486ecf8bf
https://doi.org/10.1101/2022.10.18.512627
https://doi.org/10.1101/2022.10.18.512627
Autor:
Shengcheng Dong, Alan P. Boyle
Publikováno v:
Nucleic Acids Research
Understanding the functional consequences of genetic variation in the non-coding regions of the human genome remains a challenge. We introduce h ere a computational tool, TURF, to prioritize regulatory variants with tissue-specific function by levera
Autor:
Alan P. Boyle, Shengcheng Dong
Publikováno v:
Hum Mutat
Here we present a computational model, SURF (Score of Unified Regulatory Features), that predicts functional variants in enhancer and promoter elements. SURF is trained on data from massively parallel reporter assays and predicts the effect of varian
Autor:
Shengcheng Dong, Yang Liu, Wanlu Song, Rongyao Huang, Yang Yang, Yu Liu, Yuting Wang, Xuerui Yang
Publikováno v:
International Journal of Cancer. 143:2814-2827
Although the DNA methylome profiles have been available in large cancer cohorts such as The Cancer Genome Atlas (TCGA), integrative analysis of the DNA methylome architectures in a pan-cancer manner remains limited. In the present study, we aimed to
Autor:
Ron Unger, Jay Shendure, Ayoti Patra, Beth Martin, Henry Kenlay, Zhongxia Yan, Anat Kreimer, Michael A. Beer, Nir Yosef, Dmitry Penzar, Martin Kircher, Max Schubach, Tamar Juven-Gershon, John E. Reid, Alan P. Boyle, Alex Hawkins-Hooker, Aashish N. Adhikari, Orit Adato, Nadav Ahituv, Ivan V. Kulakovskiy, Fumitaka Inoue, Chenling Xiong, Shengcheng Dong, Dustin Shigaki
Publikováno v:
Human mutation, vol 40, iss 9
Hum Mutat
Hum Mutat
The integrative analysis of high-throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory eleme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faf4702d3ab08dea7d071fe506df43ac
https://escholarship.org/uc/item/3rg6s3n3
https://escholarship.org/uc/item/3rg6s3n3