The content discusses the importance of machine learning methods in predicting 3D genome organization, focusing on enhancer-promoter interactions, chromatin loops, and topologically associating domains (TADs). Various tools and approaches are explored to predict these interactions using genomic annotations, DNA sequences, and other genomic properties. The article highlights the significance of accurate predictions for understanding gene regulation in health and disease.
The development of predictive models for chromatin interactions is essential for unraveling the complex regulatory mechanisms within the genome. Machine learning techniques leverage genomic data to enhance our understanding of enhancer-promoter interactions, chromatin loops, and TAD boundaries. These predictive models play a crucial role in deciphering the biological implications of 3D genome organization across different cell types.
Key points include the utilization of machine learning frameworks to analyze genomic annotations, DNA sequences, and epigenomic data for predicting higher-order chromatin structures. Various tools such as IM-PET, EPIANN, PETModule, LoopPredictor, PEP, SPEID, and others are discussed in detail. The content emphasizes the importance of accurate prediction models in studying gene expression regulation at a molecular level.
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arxiv.org
ข้อมูลเชิงลึกที่สำคัญจาก
by Brydon P. G.... ที่ arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03231.pdfสอบถามเพิ่มเติม