EAGER: Advanced Machine Learning Techniques to Discover Disease Subtypes in Cancer

在癌症大规模基因组和分子图谱分析中的一个重大挑战是识别不同的、分子上独立的疾病亚型，以及这些亚型与临床相关结果的关系。识别分子定义的、临床相关的亚型的障碍是特征空间的高维度、有限的样本大小以及患者之间的低复发率突变。该项目的智力优势是为癌症疾病亚型发现的高维基因表达和基因突变数据开发健壮和可扩展的基于网络的机器学习和数据挖掘技术的理论、算法和实现。该项目的结果可以帮助识别单个癌症、泛癌症和特定性别的亚型，以更好地了解癌症的性质，并制定最有效的治疗策略。本研究开发的数学和机器学习模型是生物网络诱导正则化的通用模型，适用于广泛的监督、半监督和非监督学习问题，本项目的目标是设计新的基于网络的学习模型，将关于基因调控机制的先验生物知识最佳地整合到学习算法中。研究了新的基于群和基于拉普拉斯的正则化技术以及矩阵因式分解中的受限流形学习，以设计可重现的疾病亚型模型。这是建立一个有效的癌症亚型发现工具包的第一项研究，该工具包完全集成了离散的突变图谱和连续的基因表达数据。该项目在计算机科学、数学和工程方面提供广泛的跨学科培训。随着更精确的基因组医学数据的出现，在这项研究中开发的模型可以广泛应用。

A significant challenge in the analysis of large-scale genomic and molecular profiles of cancer is the identification of distinct, molecularly independent disease subtypes and the association of these with clinically relevant outcomes. The barriers to identifying molecularly-defined, clinically relevant subtypes have been the high-dimensionality of the feature space, limited sample sizes, and low recurrence rate of mutations between patients. The intellectual merits of this project are to develop theory, algorithms, and implementation for robust and scalable network-based machine learning and data mining techniques in high-dimensional gene expression and gene mutation data for disease subtype discovery in cancer. The results of the project can help to identify individual cancer, pan-cancer, and sex-specific subtypes to better understand the nature of cancer and to develop the most efficacious therapeutic strategies. The mathematical and machine-learning models developed in this study are general biological network-induced regularization models that are applicable in a broad range of supervised, semi-supervised, and unsupervised learning problems.The goal of this project is to design novel network-based learning models that optimally integrate prior biological knowledge on gene regulatory mechanisms into learning algorithms. New group-based and Laplacian-based regularization techniques and restricted manifold learning in matrix factorization are investigated to design reproducible models for disease subtyping. This is the first study to build an efficient toolkit for cancer subtype discovery that fully integrates discrete mutational profiles and continuous gene expression data. The project provides extensive cross-disciplinary training in Computer Science, Mathematics, and Engineering. The models developed during this study can be broadly applied as more precision genomic medicine data become available.