Chemometric Strategies for the Analysis of Multivariate Chemical and Biological Measurements

用于分析多元化学和生物测量的化学计量策略

• 批准号: 46316-2013
• 负责人: Wentzell, Peter
• 金额: $ 3.21万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=632561
• 关键词: Chemometric; Strategies; Analysis; Multivariate; Chemical

Developments in analytical measurement science have led to unprecedented advances in our understanding of biological systems and medicine. The ability to measure increasing numbers of biological molecules at decreasing concentration levels in complex systems provides new insights into the function of biological organisms, and these capabilities will continue to expand. However, the collection and interpretation of relevant data in the so-called "omics" sciences remains a challenge for high-throughput biological methods. This research addresses those challenges through the development of novel approaches to visualize and interpret the massive amounts of data, transforming measurements into information. Additionally, new strategies are proposed to increase the throughput and utility of methods employed in proteomics. The core of this work draws on the applicant's expertise in the fields of chemometrics and bioinformatics, which relate to the application of statistical, mathematical and computer-based methods for the extraction of information from chemical and biological measurements, respectively. Modern analytical systems typically provide measurements on thousands of chemical entities (genetic material, proteins, metabolites, etc.) in a single biological sample, referred to as multivariate or high-dimensional data. Even so, the critical information sought (e.g. identification of a disease biomarker) may not be obtained for several reasons that include: (1) the absence of the critical molecule(s) in the data, (2) the inability to distinguish the important signal(s) from the meaningless ones, and (3) a statistically insufficient number of samples due to long analysis times. This research will address these issues by enhancement of the measurement and data analysis methods used omics research, leading to the extraction of more reliable information. Ultimately, this is expected to have important implications for the study of biology and medicine.

分析测量科学的发展导致了我们对生物系统和医学的理解前所未有的进步。在复杂系统中，测量浓度降低的生物分子数量增加的能力为生物生物的功能提供了新的见解，这些能力将继续扩展。但是，对高​​通量生物学方法的所谓“ Omics”科学中相关数据的收集和解释仍然是一个挑战。这项研究通过开发新的方法来解决这些挑战，以可视化和解释大量数据，从而将测量结果转化为信息。此外，提出了新的策略来增加蛋白质组学中使用的方法的吞吐量和效用。这项工作的核心借鉴了申请人在化学计量学和生物信息学领域的专业知识，这与分别用于从化学和生物学测量中提取信息的统计，基于数学和计算机的方法有关。现代分析系统通常在单个生物样品中对数千种化学实体（遗传物质，蛋白质，代谢物等）提供测量，称为多变量或高维数据。即便如此，由于以下几个原因，可能无法获得所寻求的关键信息（例如，鉴定疾病生物标志物）：（1）数据中缺少关键分子，（（2）由于长期分析时间的统计量不足，因此无法将重要信号与无意义的信号区分开。这项研究将通过加强使用OMICS研究的测量和数据分析方法来解决这些问题，从而导致提取更可靠的信息。最终，预计这将对生物学和医学研究具有重要意义。