Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)

阿尔茨海默氏病深度学习衍生的神经影像内表型的遗传学（家长资助）

• 批准号: 10827718
• 负责人: MYRIAM FORNAGE
• 金额: $ 38.06万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10827718
• 关键词: Administrative Supplement; Affect; Alzheimer' s Disease; Alzheimer’s disease biomarker; American; Architecture; Area; Award; Benchmarking; Biology; Caregivers; Clinical; Clinical Trials; Collaborations; Communities; Computer software; Coupled; Data; Data Set; Dementia; Elderly; Functional disorder; Funding; Genetic; Genetic Markers; Genetic study; Goals; Grant; Graph; Healthcare; Heritability; Image; Impaired cognition; Investigation; Link; Machine Learning; Magnetic Resonance Imaging; Memory; Methods; Modeling; Outcome Measure; Parents; Patients; Phenotype; Prevention strategy; Process; Prognosis; Public Health; Reproducibility; Research; Resources; Software Tools; Standardization; Suggestion; Testing; Time; U-Series Cooperative Agreements; United States National Institutes of Health; Work; artificial intelligence algorithm; biobank; brain magnetic resonance imaging; clinical predictors; data standards; deep learning; disorder risk; endophenotype; genetic architecture; genetic association; genome wide association study; genomic locus; high dimensionality; human old age (65+); imaging genetics; improved; interest; learning strategy; multimodal neuroimaging; neural; neuroimaging; novel; outcome prediction; parent grant; programs; response; synergism; therapeutic development; trait; vector; whole genome

Project Summary Alzheimer’s disease (AD) is characterized by the progressive impairment of cognitive and memory functions and is the most common form of dementia in the elderly. It affects 5.6 million Americans over the age of 65 and exacts tremendous and increasing demands on patients, caregivers, and healthcare resources, making this condition among the most significant public health problems of our time. Despite extensive studies, our understanding of the biology and pathophysiology of AD is still limited, hindering advances in the development of therapeutic and preventive strategies. Genetic studies of AD have successfully identified 40 novel loci but these explain only a fraction of the overall disease risk, suggesting opportunities for additional discoveries. Advanced neuroimaging is an essential part of current AD clinical and research investigations, which generally focus on relatively few imaging phenotypes developed by neuro- radiologists. However, there is a growing interest in exploiting the high-content information in large-scale, high dimensional multimodal neuroimaging data to identify novel AD biomarkers. Deep learning (DL) methods, an emerging area of machine learning research, uses raw images to derive optimal vector representations of imaging contents, which can be used as informative AD endophenotypes. The overall goal of the proposed supplement is to benchmark the AI algorithms we are developing on a standardized neuroimaging dataset. We will work on two topics: Predicting clinical decline (prognosis) from baseline T1-weighted brain MRI, and Discovery of genetic loci in whole- genome sequence data associated with brain MRI-derived endophenotypes. This is a collaboration with the other two U01 awards to improve the rigor and reproducibility. We will make the software tools and results publicly available. This will positively impact the larger research community.

项目总结

项目成果

The spread of COVID-19 vaccine information in Arabic on YouTube: A network exposure study.

YouTube 上阿拉伯语 COVID-19 疫苗信息的传播：一项网络曝光研究。

• DOI: 10.1177/20552076231205714
• 发表时间: 2023-01
• 期刊: DIGITAL HEALTH
• 影响因子: 3.9
• 作者: Zeid, Nour;Tang, Lu;Amith, Muhammad Tuan
• 通讯作者: Amith, Muhammad Tuan

Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.

• DOI: 10.1038/s41598-022-20646-1
• 发表时间: 2022-09-27
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Tang, Yi-Ching;Powell, Reid T.;Gottlieb, Assaf
• 通讯作者: Gottlieb, Assaf

Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies

• DOI: 10.48550/arxiv.2309.15132
• 发表时间: 2023-09
• 期刊: ArXiv
• 作者: Yaochen Xie;Z. Xie;Sheikh Muhammad Saiful Islam;D. Zhi;Shuiwang Ji

Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning.

机器学习辅助挖掘产孢梭菌的代谢能力。

• DOI: 10.1002/anie.202319925
• 发表时间: 2024
• 期刊: Angewandte Chemie (International ed. in English)
• 作者: Ouyang,Huanrong;Xu,Zhao;Hong,Joshua;Malroy,Jeshua;Qian,Liangyu;Ji,Shuiwang;Zhu,Xuejun
• 通讯作者: Zhu,Xuejun