III: Small: Collaborative Research: High-Dimensional Machine Learning Methods for Personalized Cancer Genomics

III：小：协作研究：个性化癌症基因组学的高维机器学习方法

• 批准号: 1717206
• 负责人: Quanquan Gu
• 金额: $ 30万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717206&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Dimensional

The key to success in personalized and precision cancer genomics lies in: (1) discovering and understanding the molecular-level mechanisms of how genetic alterations influence various cellular processes relevant to cancer, and (2) utilizing molecular signatures to tailor more personalized treatment strategies for patients. In order to achieve these goals, various high-throughput experimental methods have been developed in recent years to obtain information about a patient's cancer genome sequence, mRNA expression, protein expression, epigenetic readout, and other detailed information about a patient's tumor. However, algorithms that fully harness such a massive amount of high dimensional data to yield biomedical insights are often lacking. This project will advance the field of data-driven complex modeling of cancer genomic data for personalized cancer treatment by developing novel algorithms that use emerging and new techniques in high-dimensional machine learning. The results of this research have the potential to impact both the machine learning field and the computational genomics field. The educational components integrated with the research program will develop new curriculum materials, involve undergraduate students and underrepresented groups in research, and train a new generation of interdisciplinary graduate researchers. This project consists of two synergistic research thrusts to develop novel high-dimensional machine learning algorithms for analyzing high-throughput cancer genomic data. First, the project will develop high-dimensional graphical models for multi-view data modeling to integrate data from heterogeneous genome-wide data sources. Second, it will devise novel high-dimensional collaborative learning methods for personalized drug recommendation. The high-dimensional graphical models will be used to estimate networks for different cancer subtypes. These networks will then be integrated into the recommendation algorithms, which in turn will help improve the multi-view graphical model estimation. This project will enhance the ability to interpret large-scale cancer genomics data by pinpointing the roles of complex molecular interactions in cancer onset and progression, which will enable novel ways to more effectively discover personalized molecular signatures and more targeted potential treatments of cancer. Such technical innovation and conceptual advancement have the potential to reshape the way that one approaches graphical model estimation and its role in biological contexts. The project will potentially open up new possibilities for both theoreticians and practitioners in machine learning and computational biology as well as other disciplines.

个性化和精确癌症基因组学成功的关键在于：（1）发现和理解基因改变如何影响与癌症相关的各种细胞过程的分子水平机制，以及（2）利用分子特征为患者量身定制更个性化的治疗策略。为了实现这些目标，近年来已经开发了各种高通量实验方法，以获得关于患者的癌症基因组序列、mRNA表达、蛋白质表达、表观遗传读数的信息以及关于患者肿瘤的其他详细信息。然而，通常缺乏充分利用如此大量的高维数据来产生生物医学见解的算法。该项目将通过开发使用高维机器学习中新兴和新技术的新算法，推进癌症基因组数据的数据驱动复杂建模领域，以实现个性化癌症治疗。这项研究的结果有可能影响机器学习领域和计算基因组学领域。与研究计划相结合的教育部分将开发新的课程材料，让本科生和代表性不足的群体参与研究，并培养新一代跨学科研究生。该项目包括两个协同研究方向，旨在开发新的高维机器学习算法，用于分析高通量癌症基因组数据。首先，该项目将开发用于多视图数据建模的高维图形模型，以集成来自异构基因组数据源的数据。第二，它将设计新的高维协作学习方法的个性化药物推荐。高维图形模型将用于估计不同癌症亚型的网络。 然后，这些网络将被集成到推荐算法中，这反过来将有助于改善多视图图形模型估计。该项目将通过精确定位复杂分子相互作用在癌症发病和进展中的作用，增强解释大规模癌症基因组学数据的能力，这将使新的方法能够更有效地发现个性化的分子特征和更有针对性的潜在癌症治疗方法。这种技术创新和概念进步有可能重塑图形模型估计方法及其在生物学背景下的作用。该项目将为机器学习和计算生物学以及其他学科的理论家和实践者开辟新的可能性。

项目成果

Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow

• 发表时间: 2018-03
• 期刊: ArXiv
• 作者: Xiao Zhang;S. Du;Quanquan Gu

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization

• 发表时间: 2018-07
• 作者: Jinghui Chen;Pan Xu;Lingxiao Wang;Jian Ma;Quanquan Gu