It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

中文翻译：

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与复杂疾病相关的药理系统的高维数据分析。

分子还原论者对高度复杂的人类生理活性（例如衰老过程）以及治疗药物功效的看法在很大程度上被过度简化，这一点已被广泛接受。当前，使用转录组，蛋白质组学，代谢组学或表观基因组流水线中的高维（HD）数据流可以实现对生物疾病和药物反应复杂性最有效的理解。如今，多个高清数据集已经普遍使用，可用于复杂疾病（例如代谢综合征，心血管疾病）和神经退行性疾病（例如阿尔茨海默氏病）。在过去的十年中，通过开发和实施高效的生物信息平台，我们查询这些高维数据流的能力得到了极大的增强。采用这些计算方法来了解与年龄有关的疾病的复杂性，提供了一种简便的机制，可以使这种病理学认识与对治疗介导信号的类似理解相辅相成。对于能够在各种数据流中产生有意义的治疗见解的信息病理学和基于药物的分析，新颖的信息学过程（例如潜在语义索引和拓扑数据分析）可能很重要。从各种数据流中阐明高清分子疾病特征可能会产生并完善新的治疗策略，这些策略的设计应考虑到对与年龄有关的疾病和药物作用的复杂性的现实认识。我们认为，信息平台应与更先进的基于化学/药物和基于表型细胞/组织的分析预测模型协同作用，以帮助从头进行药物优先处理或有效地用于干预与衰老相关的疾病。重要声明：所有疾病以及药理机制都比十年前想像的要复杂得多。随着人们对产生大量高维数据的技术（例如转录组学，蛋白质组学，代谢组学）的普遍使用的出现，现在迫切需要开发出有效的工具来欣赏这种高度细微的数据。