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.

AI2AMP-PD：使用多词和人工智能加速帕金森氏症的诊断项目摘要和摘要帕金森氏病（PD）影响了全球超过700万人，生物标志物可以增强治疗性迫切需要管道。开发用于临床使用的生物标志物是一个艰难的过程，需要评估在多个大型队列中，每个人都会为标记增添信心。加速医学合作伙伴关系帕金森氏病（AMP PD）财团提供了一个无与伦比的机会，可以快速实现这一目标难以捉摸的目标。我们假设是由标准和高级机器提供动力的功能强大的多派分类器学习算法将在基因组量表上准确鉴定与PD相关的生物标志物。成绩单和基因组与PD相关的分类器将在早期，未经治疗的多巴胺转运蛋白患者中识别 PPMI队列中代表的神经影像学支持的诊断。成绩单和基因组分类器将是严格复制在独立的PDBP和生物界人群中。使用PD-的多词分类器相关的转录组变化和与PD相关的基因组变体将使用最先进的深度建造学习技术（例如变分自动编码器）。该分析将首次有力地描述--- - 全面编码和新颖的编码在统一的大规模数据集中，与PD相关的非编码RNA在循环血细胞中可检测到。它将开发和测试高度创新的多摩斯分类器，并提供通常有用的计算大规模，公正的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）

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

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