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

个人化和精确的癌症基因组学成功的关键在于：(1)发现和了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)利用分子签名为患者量身定做更个性化的治疗策略。为了实现这些目标，近年来开发了各种高通量实验方法，以获得关于患者癌症基因组序列、mRNA表达、蛋白质表达、表观遗传学读数和其他有关患者肿瘤的详细信息的信息。然而，完全利用如此大量的高维数据来产生生物医学见解的算法往往是缺乏的。该项目将通过开发在高维机器学习中使用新兴和新技术的新算法，推动用于个性化癌症治疗的癌症基因组数据的数据驱动的复杂建模领域。这项研究的结果有可能对机器学习领域和计算基因组学领域产生影响。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的研究生研究人员。该项目由两个协同研究推力组成，旨在开发用于分析高通量癌症基因组数据的新型高维机器学习算法。首先，该项目将开发用于多视图数据建模的高维图形模型，以整合来自不同基因组范围的数据源的数据。其次，它将设计出新颖的高维协作学习方法，用于个性化药物推荐。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进多视图图形模型估计。该项目将通过明确复杂的分子相互作用在癌症发生和发展中的作用来增强解释大规模癌症基因组数据的能力，这将使新的方法能够更有效地发现癌症的个性化分子特征和更有针对性的潜在治疗方法。这种技术创新和概念进步有可能重塑人们接近图形模型估计的方式及其在生物学背景下的作用。该项目可能会为机器学习、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。

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.