Uncovering therapeutic-associated biomarkers via machine learning and feature engineering approaches

项目总结 识别具有诊断性、健壮性和跨个体通用性的生物标记物 治疗价值是医学界最想要的努力。然而，在这方面存在许多挑战 识别这种与治疗相关的生物标记物(TAB)。例如，当前的大多数方法 寻求在一般患者队列中实现统计上显著的差异生物信号，但失败了 承认个体患者具有不同的遗传背景和表型多样性。我们最近 使用新开发的基于机器学习的特征工程方法进行研究，并在 对12种癌症类型的泛癌症研究表明，生物限制特征(在此命名 不变特征)在疾病中普遍存在，并可用于对个别癌症进行分类。重要的是，我们还 展示了不变特征可用于构建从头开始生物网络并发现 可以成功地用于推断相关基因的表达。因此，不变要素可以充当 信息编码者。利用药物再利用中心的信息，我们表明这些中心基因也是 毒品目标。总而言之，这些观察结果表明，不变功能中心可能是TAB候选对象。我们 提出在新的生物约束条件下，可以采用一种动态的方法进行生物标志物的研究 这一发现概括了个体患者的遗传异质性和分子波动。 我们的中心假设是，疾病状态表现出其分子活动的限制，并且是可识别的 不变的特征具有诊断和治疗价值。这项提案的主要目标是揭示 使用选定的NIH共同基金数据集(即exRNA、GTEx、LINC和IDG)的标签。在目标1中，我们将 测试这一假设，即生物约束的不变特征对大多数生物状态是通用的，如果不是所有生物状态的话。 我们将通过从选定的共同基金中找到关于每个生物状态的不变特征来说明这一点 数据集。我们将在疾病状态和正常状态下进行比较分析，以剖析特定疾病 不变的特征。接下来，在目标2中，我们将测试不变特征中心是制表符的假设。我们会展示的 这是通过确定不变特征中心的诊断能力来确定它们重建 其相关不变特征基因在不同患者个体中的表达 相同的疾病类型。最后，我们将把这些不变功能中心映射到IDG和DrugBank，以确定它们的 可吸性。对于那些未被研究但没有已知药物的中心，我们将进行计算分析，如 同源建模和机器学习来表征它们的可吸药性。我们期望及时完成任务。 提出的目标和该项目的成功完成无疑将为入选的 共同基金数据集，同时提供生物标记物和治疗靶点发现的新范式转变。

PROJECT SUMMARY Identifying biomarkers that are diagnostic, robust and generalizable across individuals while possessing therapeutic values is the most wanted endeavor in medicine. However, there are numerous challenges in the identification of such robust therapy-associated biomarkers (TABs). For example, most of the current methods seek to achieve statistically significant differential biological signals in general patient cohorts but fail to acknowledge heterogenous genetic backgrounds and phenotypic diversity among individual patients. Our recent studies using newly developed machine learning-based feature engineering approaches and conducted in a pan-cancer study across 12 cancer types showed that biologically constrained features (named herein invariant features) are universal in disease and can be used to classify individual cancers. Importantly, we also show that invariant features can be used to build de novo biological networks and discover network hubs that can be successfully utilized to infer the expression of associated genes. As such, invariant features can act as information encoders. Using information from Drug Repurposing Hub we show that these hub genes are also drug targets. Collectively, these observations suggest that invariant feature hubs can be TAB candidates. We propose that under the new light of biological constraints, we can use a dynamic approach for biomarker discovery that encapsulates both the genetic heterogeneity and molecular fluctuation across individual patients. Our central hypothesis is that disease states show constrains in their molecular activities, and identifiable invariable features possess diagnostic and therapeutic values. The main objective of this proposal is to uncover TABs using selected NIH Common Fund datasets (namely, exRNA, GTEx, LINC, and IDG). In Aim 1, we will test the hypothesis that biologically constrained invariant features are universal to most if not all biological states. We will show this by finding invariant features with respect to each biological state from selected Common Fund datasets. We will conduct comparative analyses in disease and normal states in order to dissect disease-specific invariant features. Next, in Aim 2, we will test the hypothesis that invariant feature hubs are TABs. We will show this by determining the diagnostic capability of invariant feature hubs for their “encodability” to reconstruct the expression values of their associated invariant feature genes in different individual patients diagnosed under same disease type. Finally, we will map these invariant feature hubs to IDG and DrugBank to determine their druggability. For those understudied hubs with no known drugs, we will perform computational analyses such as homology modeling and machine learning to characterize their druggability. We expect timely accomplishment of proposed aims and successful completion of this project will no doubt provide added values for the selected Common Fund datasets, while providing a new paradigm shift of biomarker and therapeutic target discovery.