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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568951
• 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使我们对生物系统和医学的理解取得了前所未有的进步。 在复杂系统中，以递减的浓度水平测量越来越多的生物分子的能力为生物有机体的功能提供了新的见解，这些能力将继续扩展。 然而，在所谓的“组学”科学中收集和解释相关数据仍然是高通量生物方法的挑战。 这项研究通过开发新的方法来可视化和解释大量数据，将测量结果转化为信息，从而解决了这些挑战。 此外，提出了新的策略，以增加蛋白质组学中采用的方法的吞吐量和效用。 这项工作的核心是利用申请人在化学计量学和生物信息学领域的专门知识，这两个领域分别涉及应用统计、数学和基于计算机的方法从化学和生物测量中提取信息。 现代分析系统通常提供对数千种化学实体（遗传物质、蛋白质、代谢物等）的测量。在单个生物样本中，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，疾病生物标志物的鉴定）：（1）数据中缺乏关键分子，（2）无法区分重要信号与无意义信号，以及（3）由于分析时间长，样本数量在统计学上不足。 本研究将通过改进组学研究的测量和数据分析方法来解决这些问题，从而提取更可靠的信息。 最终，预计这将对生物学和医学研究产生重要影响。