Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning-Reference-Cited by-同舟云学术

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Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning

Published:2018-11 Issue:5 Volume:7 Page:510-520.e4
ISSN:2405-4712
Container-title:Cell Systems
language:en
Short-container-title:Cell Systems

Author:

Mao Miaowei,Hu Yue,Yang Yun,Qian Yajie,Wei Huanhuan,Fan Wei,Yang Yi,Li Xiaoling,Wang Zefeng

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

National Postdoctoral Program for Innovative Talents

Intramural Research Program of National Institute of Environmental Health Sciences of the National Institutes of Health of USA

Chinese Scholarship Council Scholarship

NIH

CAS Pioneer 100-Talent

Publisher

Elsevier BV

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Reference56 articles.

1. Alternative splicing as a regulator of development and tissue identity;Baralle;Nat. Rev. Mol. Cell Biol.,2017

2. Detained introns are a novel, widespread class of post-transcriptionally spliced introns;Boutz;Genes Dev.,2015

3. Coordinated splicing of regulatory detained introns within oncogenic transcripts creates an exploitable vulnerability in malignant glioma;Braun;Cancer Cell,2017

4. Regulation of vertebrate nervous system alternative splicing and development by an SR-related protein;Calarco;Cell,2009

5. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins;Castello;Cell,2012

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4. Emerging roles of alternative RNA splicing in oral squamous cell carcinoma;Frontiers in Oncology;2022-11-25

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