权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

AI2AMP-PD: Accelerating Parkinsons Diagnosis using Multi-omics and Artificial Intelligence

AI2AMP-PD：利用多组学和人工智能加速帕金森病诊断

基本信息

批准号：
10157680
负责人：
Xianjun Dong
金额：
$ 53.7万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10157680
关键词：
Affect Artificial Intelligence Biological Markers Blood Cells Blood specimen Brain Brain Diseases Clinical Clinical Trials Code Cognitive Cross-Sectional Studies DNA Data Analytics Data Set Development Diagnosis Disease Enhancers Evaluation Future Gene Expression Genes Genetic Risk Genetic Transcription Goals Link Logistic Regressions Machine Learning Medicine Modeling Motor Nature Neurosciences Parkinson Disease Patients Population Process Public Health RNA Sampling Severities Techniques Testing Therapeutic Time Transcript Untranslated RNA Variant analytical method autoencoder base biomarker development biomarker discovery cohort computer framework deep learning differential expression disease diagnosis dopamine transporter feature selection genetic variant genome-wide genomic signature innovation large datasets large scale data learning strategy machine learning algorithm machine learning method multiple omics neuroimaging novel predictive modeling risk variant screening transcriptome

项目摘要

AI2AMP-PD: Accelerating Parkinson’s Diagnosis using Multi-omics and Artificial Intelligence PROJECT SUMMARY AND ABSTRACT Parkinson’s disease (PD) affects more than 7 million people worldwide, and biomarkers to bolster the therapeutic pipeline are urgently needed. Developing biomarkers for clinical use is a difficult process that requires evaluation of multiple, large cohorts, each adding confidence to the marker. The Accelerating Medicine Partnership in Parkinson’s disease (AMP PD) consortium provides an unparalleled opportunity to rapidly achieve this previously elusive goal. We hypothesize that a powerful, multi-omics classifier powered by standard and advanced machine learning algorithms will accurately identify PD-associated biomarkers at genome scale. Transcripts and genomic classifiers associated with PD will be identified in early-stage, untreated, patients with Dopamine Transporter- neuroimaging-supported diagnosis represented in the PPMI cohort. Transcripts and genomic classifiers will be rigorously replicated in the independent PDBP and BioFIND cohorts. Multi-omics classifiers using both PD- associated transcriptome changes and PD-associated genomic variants will be built with state-of-the-art deep learning techniques (e.g. variational autoencoder). This analysis will powerfully delineate --- for the first time --- the full spectrum of known and novel, coding and noncoding RNAs linked to PD and detectable in circulating blood cells in a harmonized, large-scale data set. It will develop and test highly innovative multi-omics classifiers and provide a generally useful computational framework for large-scale, unbiased PD biomarker discovery.

AI 2AMP-PD：使用多组学和人工智能加速帕金森病的诊断项目总结和摘要帕金森病（PD）影响着全球700多万人，生物标志物可支持治疗管道是迫切需要的。开发用于临床的生物标志物是一个困难的过程，需要进行评估多个大的队列，每个队列都增加了标记的信心。加速医学伙伴关系，帕金森病（AMP PD）联盟提供了一个无与伦比的机会，迅速实现这一先前难以捉摸的目标我们假设，一个强大的，多组学分类器由标准和先进的机器供电，学习算法将在基因组规模上准确地识别PD相关的生物标志物。转录本和基因组将在早期、未经治疗的多巴胺转运蛋白患者中确定与PD相关的分类器，神经影像学支持的诊断在PPMI队列中代表。转录本和基因组分类器将被在独立的PDBP和BioFIND队列中严格复制。多组学分类器使用PD- 相关的转录组变化和PD相关的基因组变异将建立与国家的最先进的深学习技术（例如变分自动编码器）。这种分析将有力地描绘-第一次-已知的和新的编码的全部谱，以及与PD相关的非编码RNA，并在协调的大规模数据集中在循环血细胞中检测到。它将开发和测试高度创新的多组学分类器，并提供一个普遍有用的计算大规模、无偏倚的PD生物标志物发现的框架。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis.

DOI：
10.1093/bioinformatics/btab385
发表时间：
2021-11-18
期刊：
BIOINFORMATICS
影响因子：
5.8
作者：
Dong, Xianjun;Li, Xiaoqi;Chang, Tzuu-Wang;Scherzer, Clemens R.;Weiss, Scott T.;Qiu, Weiliang
通讯作者：
Qiu, Weiliang

FLED: a full-length eccDNA detector for long-reads sequencing data.

FLED：用于长读长测序数据的全长 eccDNA 检测器。

DOI：
10.1093/bib/bbad388
发表时间：
2023
期刊：
Briefings in bioinformatics
影响因子：
9.5
作者：
Li,Fuyu;Ming,Wenlong;Lu,Wenxiang;Wang,Ying;Li,Xiaohan;Dong,Xianjun;Bai,Yunfei
通讯作者：
Bai,Yunfei