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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615442
• 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）将原发性肿瘤数据（如基因表达数据）与以网络形式提供的次级数据（如蛋白质相互作用网络或其他生物分子网络）相结合，已被提出用于样本分类预测（如癌症结局预测）。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（NBs），它可以最好地区分类别。不幸的是，目前在癌症生物信息学中提出的NC方法在所选nb的鲁棒分类性能和稳定性方面取得了非常有限的进展。目前用于将原发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方法的性能明显受到当前蛋白质相互作用网络的高噪声和稀疏性的影响。网络不完备性是当前网络控制的一个重要限制。我的目标是改进现有的和设计新的NC方法，以减轻这些限制；通过加入对肿瘤分类有用的附加生物数据（三级数据），然后设计适当的整合和学习方法，以最好地捕获数据中包含的生物信息。对于特定的癌症疾病，找到准确且可靠的预测性NBs将更好地表征其亚型、结局或分期。NBs将帮助医生更准确地诊断癌症，或建议更好的治疗方法，也将成为未来潜在的药物靶点。NBs描述了基因之间的功能依赖性，可以在疾病发展过程中随着时间的推移进行监测，从而为癌症治疗提供更好的策略，并为疾病的早期发现提供新的策略。