Multi-Source Sparse Learning to Identify MCI and Predict Decline

 描述（由申请人提供）：轻度认知障碍（MCI）患者进展为痴呆的风险较高。MCI提供了一个早期靶向疾病过程的机会。临床医生和研究人员正在加紧努力，以发现MCI的预防性治疗。这些努力产生了大量的数据-多种模式的大脑图像，以及蛋白质组学，遗传学和神经认知数据，这些数据为以更高的精度和预测能力研究MCI相关问题提供了前所未有的机会。了解其重要性，NIH于2003年资助了阿尔茨海默病神经影像学倡议（ADNI），以促进对MCI和AD发病和进展的各种生物标志物的科学评估。为了实现这样一个雄心勃勃的愿景，迫切需要多源融合和疾病生物标志物发现框架。虽然有希望，但来自多个异构数据源的大量不完整数据给科学家和工程师带来了巨大的挑战。例如，ADNI-1数据（与许多其他大型数据集一样）表现出区块缺失模式：大多数受试者具有MRI、遗传信息;约一半受试者具有CSF测量;另一半受试者具有FDG-PET;一些受试者具有蛋白质组学数据。虽然有许多生物信息学工具可用，但没有现有的工具提供有效的方法来融合多源不完整数据用于疾病生物标志物发现。在这里，我们的目标是开发一种新的计算框架，以整合和分析多个，异构，大容量，不完整的生物医学数据的MCI的早期检测。我们的4个主要目标是：（1）开发新的结构化稀疏学习公式的多源融合。计算方法将识别生物标志物以将多源数据与MCI相关联。将开发新的稀疏筛选方法，以将所提出的配方扩展到非常高维的数据。(2)开发计算方法以整合网络数据。我们将开发新的方法，将现有的生物学知识，如表示为网络的途径到预测模型。将网络结构作为先验知识约束模型参数，进一步提高预测能力。(3)开发计算方法来整合多个不完整的数据源。所提出的计算框架将集成多个异构数据与块式缺失模式。该框架将多个不完整数据源融合问题转化为多任务学习问题，首先将预测问题分解为一组任务，然后同时为所有任务建立模型。(4)开发和传播用于多源融合和生物标志物识别的软件工具。这些软件工具将用于MCI的早期检测，并将通过几个临床研究项目进行验证。我们的开源软件将免费提供给研究社区，包括我们庞大的现有用户社区。我们目前的软件包之一SLEP拥有来自25个国家的约4，500名活跃用户。我们的软件工具将很容易适用于分析来自其他神经和精神疾病的多源数据。

 DESCRIPTION (provided by applicant): Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to dementia. MCI offers an opportunity to target the disease process early. Clinicians and researchers are intensifying their efforts to detect MCI pre-symptomatically in order to develop preventive treatments. These efforts generate a large amount of data - brain images of multiple modalities, and proteomics, genetic, and neurocognitive data that provide unprecedented opportunities to investigate MCI-related questions with greater precision and predictive power. Understanding its importance, NIH in 2003 funded the Alzheimer's Disease Neuroimaging Initiative (ADNI) to facilitate scientific evaluation of various biomarkers for the onset and progression of MCI and AD. To realize such an ambitious vision, there is an urgent need for multi-source fusion and disease biomarker discovery frameworks. While promising, large volumes of incomplete data from multiple heterogeneous data sources pose huge challenges to scientists and engineers. For instance, the ADNI-1 data (like many other large datasets) exhibit a block-wise missing pattern: most subjects have MRI, genetic information; about half of the subjects have CSF measures; a different half of the subjects have FDG-PET; and some subjects have proteomics data. Although many bioinformatics tools are available, no existing tools offer an effective way to fuse multi-source incomplete data for disease biomarker discovery. Here we aim to develop a novel computational framework to integrate and analyze multiple, heterogeneous, large volume, incomplete biomedical data for early detection of MCI. Our 4 primary aims are: (1) Develop novel structured sparse learning formulations for multi-source fusion. The computational methods will identify biomarkers to correlate multi-source data with MCI. Novel sparse screening methods will be developed to scale the proposed formulations to very high-dimensional data. (2) Develop computational methods to integrate network data. We will develop novel methods for incorporating existing biological knowledge such as pathways represented as networks into the prediction model. The network structure will be used as prior knowledge to constrain model parameters, to further improve predictive power. (3) Develop computational methods to integrate multiple incomplete data sources. The proposed computational framework will integrate multiple heterogeneous data with a block-wise missing pattern. The proposed framework formulates the multiple incomplete data source fusion problem as a multi-task learning problem by first decomposing the prediction problem into a set of tasks, then building the models for all tasks simultaneously. (4) Develop and disseminate software tools for multi-source fusion and biomarker identification. The software tools will be used for early detection of MCI and will be validated by several clinical research projects. Our open source software will be made freely available to the research communities, including our large community of existing users. One of our current packages, SLEP, has ~4,500 active users from ~25 countries. Our software tools will be easily adaptable for analyzing multi-source data from other neurological and psychiatric disorders.