Biomedical Computing and Informatics Strategies for Precision Medicine

基因组方法在精确医学中的应用将关键取决于我们识别基因的能力。 其表达影响常见疾病的发生、发展和严重程度，如散发性疾病 癌症。在过去的20年里，发展了许多强大的计算和统计方法 几年来协助这一努力。然而，这些方法中的绝大多数集中在错误或相关的 敏感度和特异度等衡量模型质量的指标。这些措施很重要，但 不要捕捉可能对生物医学研究人员有意义的其他模型质量衡量标准 医生。我们在这里建议开发一种全面的方法来建模基因组数据，该方法考虑了 同时考虑模型质量的多个客观和主观衡量标准。这是我们的工作 假设多目标方法将产生更一致、更具可重复性的结果 更大的临床影响。具体地说，我们将开发一种新的分层Pareto优化(HiParOp) 一种算法，能够为给定的基因表达和计算模型集成多个标准 临床结果(目标1)。这种方法将首先用模拟的基因表达数据进行验证，这些数据反映了 癌症的层级复杂性。然后，我们将通过将HiParOp算法应用于几个 经过充分研究和充分描述的乳腺癌数据集，导致了诊断测试和新药的出现 目标(目标2)。在这里，我们将包括一长串模型质量衡量标准，其中包括传统目标 诸如肿瘤聚集性或区分性之类的指标，以及诸如 作为临床相关性和可药性。将HiParOp应用于研究充分的癌症的经验 已取得的进展将用于对算法进行进一步改进。然后，我们将应用 HiParOp方法用于非小细胞肺癌(NSCLC)的基因组分析 改善诊断和治疗的机会。我们将分析几个精心进行的基因 非小细胞肺癌组织中的表达研究(AIM 3)。最后，我们将开发并发布一个R包，它将 允许其他人轻松实现HiParOp方法(AIM 4)。

The use of genomic measures for precision medicine will depend critically on our ability to identify genes whose expression impacts the initiation, progression, and severity of common diseases such as sporadic cancer. A multitude of powerful computational and statistical methods have been developed over the last 20 years to assist with this endeavor. However, the vast majority of these approaches focus on error or related measures such as sensitivity and specificity as a measure of model quality. These measures are important but do not capture other measures of model quality that may be meaningful to biomedical researchers and physicians. We propose here to develop a comprehensive approach to modeling genomics data that takes into consideration multiple objective and subjective measures of model quality simultaneously. It is our working hypothesis that multiobjective methods will yield results that are more consistent, more reproducible, and with greater clinical impact. Specifically, we will develop a novel Hierarchical Pareto Optimization (HiParOp) algorithm that is capable of integrating multiple criteria for a given computational model of gene expression and clinical outcomes (AIM 1). This approach will first be validated with simulated gene expression data that reflect the hierarchical complexity of cancer. We will then evaluate the HiParOp algorithm by applying it to several well-studied and well-characterized breast cancer data sets that have led to diagnostic tests and new drug targets (AIM 2). Here, we will include a long list of measures of model quality that include traditional objective measures such as the cohesiveness or distinctiveness of tumor clusters as well as subjective measures such as clinical relevance and druggability. Experience applying HiParOp to a well-studied cancer where significant progress has been made will be used to make further refinements to the algorithm. We will then apply the HiParOp approach to the genomic analysis of non-small cell lung cancer (NSCLC) where there is substantial opportunity for improved diagnosis and treatment. We will analyze several carefully conducted gene expression studies in NSCLC cancer tissue (AIM 3). Finally, we will develop and release an R package that will allow others to easily implement the HiParOp method (AIM 4).