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Titlebook: Research in Computational Molecular Biology; 21st Annual Interna S. Cenk Sahinalp Conference proceedings 2017 Springer International Publi

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樓主: OBESE
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發(fā)表于 2025-3-26 22:03:47 | 只看該作者
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發(fā)表于 2025-3-27 11:38:28 | 只看該作者
A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need foratus is suggested to hold great potential for better understanding the role of epigenetics. Different cell types are known to have different methylation profiles. Therefore, in the common scenario where methylation levels are collected from heterogeneous sources such as blood, convoluted signals are
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發(fā)表于 2025-3-27 16:46:21 | 只看該作者
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發(fā)表于 2025-3-27 20:52:25 | 只看該作者
Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritabted quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard error
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發(fā)表于 2025-3-28 06:00:20 | 只看該作者
AllSome Sequence Bloom Trees,hing methods. One particular example is a database of 2,652 human RNA-seq experiments uploaded to the Sequence Read Archive. Recently, Solomon and Kingsford proposed the Sequence Bloom Tree data structure and demonstrated how it can be used to accurately identify SRA samples that have a transcript o
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