DMS/NIGMS 1: Multilevel stochastic orthogonal subspace transformations for robust machine learning with applications to biomedical data and Alzheimer's disease subtyping

DMS/NIGMS 1：多级随机正交子空间变换，用于稳健的机器学习，应用于生物医学数据和阿尔茨海默病亚型分析

• 批准号: 2347698
• 负责人: Julio Castrillon
• 金额: $ 59.94万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347698&HistoricalAwards=false
• 关键词: DMS; NIGMS; Multilevel; stochastic; orthogonal

Late-onset Alzheimer's Disease (AD) is the most common form of dementia, with an estimated 6.5 million Americans aged 65 and older living with AD today - this number will double by 2050. AD occurs in more than 35% of individuals over the age of 85 and is the fifth leading cause of death among Americans over the age of 65, with a resulting societal cost of more than $340 billion per year. Over the past years, numerous studies have highlighted that there are likely different forms of AD and AD-related dementia in the context of genetics, clinical symptoms, and biochemical pathways. Different processes and molecular pathways can lead to many clinical and physiological subtypes of AD. This may help to explain numerous (failed) clinical trials which have usually targeted the well-known amyloid pathways and genes. To develop newer, more effective, and safer treatments, multiple new clinical targets for AD treatment are needed to increase probabilities of success. The need to identify such multiple processes/pathways underlying specific AD subtypes is crucial. Discovering these will allow the development of targeted diagnosis and treatment that is adapted and personalized to particular AD forms. The investigators in this multifaceted project will leverage the availability of genetic, protein and brain imaging data obtained from diverse populations to develop a mathematical foundation and protocol for identifying AD subtypes and potential drug targets tailored to these subtypes. The findings will be valuable to the medical community and will contribute to further understanding of the many different forms of AD and to advancing precision medicine approaches. The investigators are also committed to training, developing and nurturing students' expertise in these areas, providing them with valuable learning opportunities. The increasing utilization and analysis of extensive datasets, particularly in medical and biological domains, underscores the need for advanced and precise data analysis methods. In these contexts, Machine Learning (ML)-based statistical inference is rapidly becoming a cornerstone of computational value addition. However, while much attention has been devoted to refining ML algorithms, the significance of feature engineering has been somewhat overlooked. Consequently, there is a growing interest in developing a novel mathematical framework for feature construction. The key insight is to treat data as realizations of a random field in a suitable Bochner function space. By constructing a new coordinate system, the investigators can unveil well-defined patterns that can significantly enhance the accuracy of existing ML algorithms. The objectives of this project include: (I) Developing a mathematical theory and protocol for constructing innovative features to better discriminate underlying stochastic behaviors of input data, employing multilevel spaces and the Karhunen-Loeve (KL) expansion for Bochner spaces. (II) Analyzing and optimizing the parameters of such multilevel feature constructions to markedly enhance the performance of ML algorithms, especially when dealing with complex and challenging inputs. (III) Identifying ML-based subtypes of Alzheimer's Disease (AD) from available extensive AD datasets such as genome-wide genetic, genomic, proteomic, brain imaging data and population-scale electronic health record data. With an estimated 6.5 million Americans 65 and older living with AD, the impact of work in this area can potentially be very significant. Particularly, accurate subtyping of cases can greatly accelerate successful development of new targeted AD drugs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

晚期发作的阿尔茨海默氏病（AD）是痴呆症的最常见形式，估计有650万名65岁及65岁以上的AD居住在AD的美国人 - 到2050年，这个数字将增加到2050年。AD出现在85岁以上的35％以上，是65岁以上的美国人中的第五个领先的死亡原因，比65岁以上的年龄在65岁以上，一年一度的成本比3.4 $ $ 340的法案。在过去的几年中，许多研究表明，在遗传学，临床症状和生化途径的背景下，AD和与AD相关的痴呆可能存在不同形式。 不同的过程和分子途径可以导致AD的许多临床和生理亚型。这可能有助于解释许多（失败的）临床试验，这些试验通常针对众所周知的淀粉样蛋白途径和基因。为了开发更新，更有效，更安全的治疗方法，需要进行多种新的AD治疗临床目标以提高成功的概率。识别特定AD亚型的基本多个过程/途径的需求至关重要。发现这些将允许开发针对特定AD形式的针对性诊断和治疗。这个多面项目中的研究人员将利用从不同人群获得的遗传，蛋白质和脑成像数据的可用性，以开发数学基础和方案，用于识别AD亚型和针对这些亚型量身定制的潜在药物靶标。这些发现将对医学界有价值，并将有助于进一步了解广告的许多不同形式，并推进精确的医学方法。调查人员还致力于在这些领域的培训，发展和培养学生的专业知识，从而为他们提供宝贵的学习机会。 对广泛数据集的利用和分析，特别是在医学和生物领域中，强调了对高级和精确的数据分析方法的需求。在这些情况下，基于机器学习（ML）的统计推断正在迅速成为计算值增加的基石。但是，尽管已经大量关注了精炼ML算法，但功能工程的重要性被忽略了。因此，人们对开发一个新型的数学框架的特征构建越来越兴趣。关键见解是将数据视为合适的Bochner功能空间中随机字段的实现。通过构建新的坐标系，研究人员可以推出明确定义的模式，从而可以显着提高现有ML算法的准确性。该项目的目标包括：（i）开发一种数学理论和协议，用于构建创新特征，以更好地区分输入数据的潜在随机行为，采用多层次空间和Karhunen-loeve（KL）扩展Bochner空间。 （ii）分析和优化此类多级特征构建体的参数，以显着增强ML算法的性能，尤其是在处理复杂且具有挑战性的输入时。 （iii）从可用广泛的广泛的AD数据集（例如全基因组遗传，基因组，蛋白质组学，脑成像数据和人口尺度的电子健康记录数据）中鉴定出基于ML的阿尔茨海默氏病（AD）的亚型。估计有650万美国人65岁以上的AD生活，这一领域的工作影响可能非常重要。特别是，准确的案件亚型可以极大地加速新的有针对性的AD药物的成功开发。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。