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高维多平台临床基因组数据的功能整合贝叶斯分析AI 解读

Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and…

通过折中回归权重改进小域估计AI 解读

Shrinkage estimates of small domain parameters typically use a combination of a noisy “direct” estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage toward a poorly estimated regression surface. In this article, we introduce a new class of robust, empirically-driven regression weights that target…

划定入选名单：大规模推断中的排序与筛选改进AI 解读

Summary Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood approaches tend to favour units with high measurement error. Available Bayesian and empirical Bayesian…