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

已提出将原发肿瘤数据(例如，基因表达数据)与以网络(例如，蛋白质相互作用网络或其他生物分子网络)形式提供的次要数据相结合的基于网络的分类器(NCS)，用于样本类别预测(例如，癌症结果预测)。网络数据被用来识别相互作用的信息性基因组，称为网络生物标记物(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方法在选定的NBS的稳健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捉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分离阻碍了NB识别的稳定性。当前蛋白质相互作用网络的高噪声和稀疏性严重阻碍了现有NC方法的性能。网络不完备性是当前网络控制系统的一个重要缺陷。我的目标是改进现有的NC方法，并设计新的NC方法来缓解这些限制；通过包括对肿瘤分类有用的额外生物数据(第三级数据)，然后设计适当的集成和学习方法，以最好地捕获数据中包含的生物信息。对于一种特定的癌症疾病，找到准确和可靠的预测NBS将提供对其亚型、结果或阶段的更好描述。国家统计局将帮助医生更准确地诊断癌症，或提出更好的治疗建议，未来也将成为潜在的药物靶点。NBS描述了基因之间的功能依赖性，可以在疾病的发展过程中随着时间的推移进行监测，因此为癌症的治疗提供了更好的策略，并为疾病的早期发现提供了新的策略。*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，程度分布不对称。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，将节点对分为交互和非交互两类，以提高给定网络的质量。我的方法将基于“如果两个节点离彼此更近，它们就会相互作用”的想法；即，在学习框架内提出并集成不同的节点相似性度量。提出的改善网络质量的方法是新颖的，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)。

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).**