Feature Selection for Genomic Data Using Known and Novel Biological Information

描述（由申请人提供）：我们的长期目标是通过建立癌症风险和预后的准确预测模型，并根据包括临床和基因组数据在内的各种数据制定个性化的预防和治疗策略来降低癌症风险。我们当前研究的直接目标是开发创新的统计方法，以识别与癌症风险和预后相关的基因组特征，并结合生物信息，包括先前知识的基因通路和新型microRNA（miRNA）调控网络。本研究的基本原理是：1）在许多研究中已经获得了诸如基因组生物标志物的高维数据，并且在可预见的未来很可能在实践中容易获得：2）为了使用高维基因组数据建立良好的预测模型，特征选择是必要的; 3）结合已知的和新的生物信息允许在特征选择中借用信息，从而产生更大的功率;和4）半参数方法比在特征选择文献中占主导地位的参数方法对模型误指定更鲁棒。这些考虑导致四个具体目标：1）在癌症结果的半参数加速失效时间（AFT）模型中开发高维生物标志物的分层特征选择（例如，癌症复发或死亡的时间）并结合已知的和新的生物信息; 2）开发用于癌症结果的AFT模型中的高维生物标志物的贝叶斯特征选择（例如，癌症复发或死亡的时间）与miRNA调控网络的综合分析和已知的和新的生物信息的结合; 3）开发有效的算法和用户友好的软件，目的是将它们传播给癌症研究人员;和4）通过广泛的数值研究（包括模拟和真实的数据分析）对所提出的方法进行系统评估。我们提出的方法区别于现有的方法，我们使用已知的和新的生物信息来指导特征选择，我们研究了两种替代方法，半参数和完全贝叶斯联合建模，每一种都有自己的优点和缺点。所有目标的进展将由前列腺癌和脑肿瘤数据以及广泛的模拟研究指导和评估。所提出的方法将使研究人员能够识别关键的基因组特征以及预测癌症风险和预后的生物学途径，从而导致潜在的药物靶点和随后有效的个性化治疗。它们承诺为经常遇到类似数据和生物信息的广泛生物医学科学环境带来类似的好处。

DESCRIPTION (provided by applicant): Our long-term goal is to reduce cancer risk by building accurate prediction models for cancer risk and prognosis and developing individualized prevention and treatment strategies based on diverse data including clinical and genomic data. Our immediate goal in the current study is to develop innovative statistical methods to identify genomic features in relation to cancer risk and prognosis with incorporation of biological information including prior-knowledge gene pathways and novel microRNA (miRNA) regulatory network. The underlying rationale for this research is that: 1) high-dimensional data such as genomic biomarkers have been obtained in many research studies and will likely be readily available in practice in the foreseeable future; 2) feature selection is imperative in order to buid good prediction models using high-dimensional genomic data; 3) incorporating known and novel biological information allows information-borrowing in feature selection, resulting in greater power; and 4) semiparametric methods are more robust to model misspecification than parametric methods that have dominated the literature in feature selection. These considerations lead to four specific aims: 1) develop hierarchical feature selection of high-dimensional biomarkers in semiparametric accelerated failure time (AFT) models for cancer outcomes (e.g., time to cancer recurrence or death) with incorporation of known and novel biological information; 2) develop Bayesian feature selection of high- dimensional biomarkers in AFT models for cancer outcomes (e.g., time to cancer recurrence or death) with integrative analysis of the miRNA regulatory network and incorporation of known and novel biological information; 3) develop efficient algorithms and user-friendly software with the goal of disseminating them to cancer researchers; and 4) perform systematic evaluation of the proposed methods through extensive numerical studies including simulations and real data analyses. Our proposed methods distinguish themselves from existing approaches in that we use both known and novel biological information to guide feature selection, and we investigate two alternative approaches, semiparametric and fully Bayesian joint-modeling, each of which has its own strengths and weaknesses. Progress on all aims will be guided by and evaluated on motivating prostate cancer and brain tumor data, and by extensive simulation studies. The proposed methods will allow investigators to identify key genomic signatures as well as biological pathways that are predictive of cancer risk and prognosis, leading to potential drug targets and subsequently effective personalized treatments. They promise similar benefits to a wide range of biomedical science settings where similar data and biological information are often encountered.