Tisslet tissues-based learning estimation for transcriptomics

A. Miloudi

doi:10.1186/s12859-024-06025-9

2025-02-25

DOI：10.1186/s12859-024-06025-9

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摘要

En 中文

在多组学数据分析各种疾病的背景下，利用遗传预测的基因表达进行的全转录组关联研究有望识别与复杂性状相关的新型区域。然而，现有针对多组织基因表达预测的方法通常未能考虑组织-组织表达相互作用，从而限制了其准确性和有效性。本研究通过基于非线性多变量模型纳入组织-组织表达相关性，解决了跨多组织预测基因表达的挑战。我们的发现表明，该模型在估计组织-组织相互作用和准确预测缺失数据方面表现优异。这些结果对多组学数据分析及全转录组关联研究具有重要意义，提示了一种识别与复杂性状相关区域的新方法。

Keyword:

Transcriptomics

Multiple-tissues

Machine learning

Sparse covariance matrix

EQTL

Likelihood estimator

期刊

BMC Bioinformatics

IF:

3.3

论文数:

584

被引数:

5.2W

机构

carnegie mellon univ qatar

学者数:

论文数: 7

被引数: 2

fusmba

学者数:

论文数: 1

被引数: 0

hbku

学者数:

论文数: 5

被引数: 1

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