From 585c44b366ca6796ce4cef56b6d032f4b98e02ef Mon Sep 17 00:00:00 2001 From: zhongshan Date: Sat, 30 May 2020 08:20:52 +0800 Subject: [PATCH] zsl added zsl added --- content/01.abstract.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/01.abstract.md b/content/01.abstract.md index 08eba06..74dd163 100644 --- a/content/01.abstract.md +++ b/content/01.abstract.md @@ -1,3 +1,4 @@ ## Abstract {.page_break_before} +ZSL added for test. Decoding the regulatory behavior of DNA sequences and the functional effects of noncoding variants is a preeminent challenge in understanding the mechanisms of gene regulation. This is also important for the genetics of common diseases, as most disease-associated variants are located in noncoding regions of the genome. Recently, Convolutional Neural Networks (CNNs) based methods have been developed to predict genome-wide chromatin profiles in various cellular contexts. However, these tools and resources were often trained in cell lines or bulk tissues that are not necessarily disease-related. This is particularly an issue for neuropsychiatric disorders, where the most relevant cell and tissue types are missing in the training data used by current tools.