Integrative Network-Based Machine Learning Approaches for Cancer Bioinformatics and Bio-Molecular Network Reconstruction

用于癌症生物信息学和生物分子网络重建的基于网络的综合机器学习方法

• 批准号: RGPIN-2016-05017
• 负责人: Ngom, Alioune
• 金额: $ 1.6万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660871
• 关键词: Integrative; Network; Based; Machine; Learning

Network-based classifiers (NCs) that combine primary tumor data (e.g., gene expression data) with secondary data provided in the form of network (e.g., protein interaction networks or other bio-molecular networks) have been proposed for sample class prediction (e.g., cancer outcome prediction). The network data is used to identify informative groups of interacting genes, called network biomarkers (NBs), which can best separate the classes. Unfortunately, current NC methods proposed in cancer bioinformatics have produced very limited progress in terms of robust classification performance and stability of selected NBs. Current methods used for combining the primary tumor data with the network data do not well capture and summarize the biological knowledge contained within the data. The high dimensionality of the integrated data as well as the decoupling of the training of current NCs from the selection of genes hampers the stability of the NB identification. The performances of current NC methods are significantly hindered by the high level of noise and sparseness of current protein interaction networks. Network incompleteness is an important limitation in current NCs. My goal is to improve existing and devise new NC approaches which alleviate these limitations; by including additional biological data (tertiary data) useful for tumor classification, and then devising appropriate integration and learning methods which can best capture the biological information contained in the data. For a given cancer disease, finding accurate and robust predictive NBs will provide a better characterization of its subtypes, its outcomes, or its stages. The NBs will help physicians diagnose cancer more accurately, or suggest better treatment, and will also serve as potential drug targets in the future. The NBs, which describe the functional dependency between genes, can be monitored over time in the development of the disease, and hence, provide better strategies for cancer care and new strategies for the early detection of the disease.***Current biomolecular networks such as Protein-Protein Interaction (PPI) networks are incomplete, contain many false-positive interactions and even many more false-negative interactions, and are very sparse with skewed degree distributions. This reduces the performance of many network-based prediction methods. I plan to propose network-based prediction methods which classify node pairs as interacting or not, in order to improve the quality of given networks. My approach will be based on the idea that "two nodes interact if they are closer to each other"; i.e., to propose and integrate different node similarity measures within learning frameworks. The proposed methods to improve the quality of networks are novel and the reconstructed PPI networks can be used in any network-based prediction problems (e.g., protein function prediction).**

基于网络的分类器（NC），联合收割机原发性肿瘤数据（例如，基因表达数据）与以网络形式提供的次级数据（例如，蛋白质相互作用网络或其它生物分子网络）已经被提出用于样本类别预测（例如，癌症结果预测）。网络数据用于识别相互作用基因的信息组，称为网络生物标志物（NB），它可以最好地区分类别。不幸的是，目前癌症生物信息学中提出的NC方法在稳健的分类性能和所选NB的稳定性方面取得了非常有限的进展。目前用于将原发性肿瘤数据与网络数据相结合的方法不能很好地捕获和总结数据中包含的生物学知识。集成数据的高维性以及当前NC的训练与基因选择的解耦阻碍了NB识别的稳定性。目前的NC方法的性能显着阻碍了当前蛋白质相互作用网络的高水平的噪声和稀疏性。网络不完备性是当前网络控制器的一个重要缺陷。我的目标是改善现有的和设计新的NC方法，减轻这些限制，包括额外的生物数据（三级数据）有用的肿瘤分类，然后设计适当的集成和学习方法，可以最好地捕捉生物信息中包含的数据。对于给定的癌症疾病，找到准确和强大的预测NB将提供其亚型，其结果或其阶段的更好表征。NB将帮助医生更准确地诊断癌症，或建议更好的治疗方法，并将在未来成为潜在的药物靶点。NB描述了基因之间的功能依赖性，可以在疾病的发展过程中随着时间的推移进行监测，因此为癌症护理提供了更好的策略，并为疾病的早期检测提供了新的策略。目前的生物分子网络，如蛋白质-蛋白质相互作用（PPI）网络是不完整的，包含许多假阳性相互作用，甚至更多的假阴性相互作用，并且非常稀疏，具有偏斜的度分布。这降低了许多基于网络的预测方法的性能。我计划提出基于网络的预测方法，将节点对分类为相互作用或不相互作用，以提高给定网络的质量。我的方法将基于“两个节点相互作用，如果它们彼此更接近”的想法;即，在学习框架内提出并整合不同的节点相似性度量。所提出的提高网络质量的方法是新颖的，并且重构的PPI网络可以用于任何基于网络的预测问题（例如，蛋白质功能预测）。**