110 / 2024-10-30 13:22:38
G-3D: A Deep Learning Framework for Predicting 3D Genome Architecture Across Cell Types Using Transcription Factor Expression
Transcription factors (TFs),Deep learning,3D genome organization
Abstract Pending
曾彭归航 / 中山大学中山医学院
丁俊军 / 中山大学中山医学院
Transcription factor(TF) binding on chromatin impacts the recruitment of cellular machinery needed for gene transcription and histone modification, thereby affecting cell type-specific chromatin 3D structures and gene expression. Numerous studies have shown that knockdown or overexpression of individual transcription factors can influence chromatin 3D structure. However, no systematic research has yet evaluated the extent to which different transcription factor expressions impact specific DNA sequences. To achieve this goal, we utilize cross-attention to integrate DNA sequences and transcription factor expressions to predict cell type-specific chromatin 3D structures. Through this method, we identified transcription factors with significant impacts on chromatin 3D structure. We aim, to map the relationship between the expression levels of these transcription factors and the chromatin 3D structures formed by specific DNA sequences, highlighting how different transcription factors impact distinct DNA regions. We hope this model will provide insights for manipulating chromatin 3D structures artificially.

 
Important Date
  • Conference Date

    Oct 31

    2024

    to

    Nov 03

    2024

  • Nov 03 2024

    Registration deadline

Sponsored By
崖州湾国家实验室
华中农业大学
浙江大学
中国遗传学会
中国遗传学会三维基因组学专委会
Organized By
中国生物信息学基因组信息学专委会
中国遗传学会表观遗传分会
中国细胞生物学学会染色质生物学分会
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