SERVICES FOR SYNTHESIZING INFORMATION TO SUPPORT TREATMENTS FOR ALCOHOL USE DISORDER

为了响应PHS 2020-1，主题019题为“加速酒精研究的数据科学工具”，Netrias和Waggoner酒精和成瘾研究中心（WCAAR）提议开发ASSIST，这是一个数据科学平台，将使用深度学习来帮助酒精研究人员发现与酒精使用障碍（AUD）临床相关的药物靶点。ASSIST引入了一种新的深度学习算法，即深度响应模型，这是一种数据驱动的方法，用于发现临床上与AUD相关的多靶点转录组学特征。然后，这些目标可以用于确定有可能减少酒精成瘾和预防复发的新治疗方法。 最先进的生物信息学技术依赖于富集分析工具，这些工具利用不精确的知识库，从不相关的疾病和模型系统中策划，并且仅覆盖存在注释的一小部分基因。这导致在识别与AUD临床相关的靶点方面的成功率较低。相反，ASSIST将采取数据驱动的方法，直接从转录组数据中学习基因之间的更高水平映射及其对疾病状态的影响。Netrias将开发计算测试，以测量所发现的目标对AUD的准确性、鲁棒性和特异性。然后，ASSIST将与药物数据库集成，以确定将逆转所发现的靶点集的表达谱的治疗方法。

In response to PHS 2020-1,Topic 019 entitled, “Data Science Tools for Accelerating Alcohol Research”, Netrias and the Waggoner Center for Alcohol and Addiction Research (WCAAR) propose to develop ASSIST, a data science platform that will use deep learning to aid alcohol researchers in the discovery of drug targets clinically linked to Alcohol Use Disorder (AUD). ASSIST introduces a novel deep learning algorithm, the Deep Response Model, which is a data-driven approach to discover multi-target transcriptomic profiles clinically linked to AUD. These targets can then be used to identify new treatments that have the potential to reduce addiction to alcohol and prevent relapse. State-of-the-art bioinformatics techniques rely on enrichment analysis tools that utilize imprecise knowledgebases, curated from unrelated diseases and model systems, and only cover a fraction of genes for which annotation exists. This leads to a low success rate in the identification of targets clinically linked to AUD. Instead, ASSIST will take a data-driven approach to learn higher level mappings between genes and their impact on disease state directly from transcriptomic data. Netrias will develop computational tests that will measure the accuracy, robustness, and specificity of the discovered targets to AUD. ASSIST will then integrate with drug databases to identify treatments that will reverse the expression profiles of the discovered set of targets.