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Artificial Intelligence based adaptive and interpretable models for analyzing multi-track epigenomic sequential data

Artificial Intelligence based adaptive and interpretable models for analyzing multi-track epigenomic sequential data
基于人工智能的自适应和可解释模型，用于分析多轨表观基因组序列数据
批准号：
437034
负责人：
Ashraf Ahmed
金额：
$21.75万
依托单位：
University of Manitoba
依托单位国家：
加拿大
项目类别：
Operating Grants
财政年份：
2020
资助国家：
加拿大
项目状态：
已结题
起止时间：
2020-10-01 至 2023-10-01
来源：
https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=437034&lang=en
关键词：
3d Chromatin 3d Genome Artificial Intelligence Deep Learning Epigenetics Machine Learning Sequence Modeling

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英文摘要
In this project we will take a fresh approach to harness the great potential of AI in the big data-analyses of epigenomic sequences. Epigenetics is the study of molecules and mechanisms that can perpetuate alternative gene activity states in the context o
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