Automated Literature Mining for Validation of High-Throughput Function Prediction

用于验证高通量函数预测的自动文献挖掘

• 批准号: 7724794
• 负责人: Karin Maria Verspoor
• 金额: $ 72.14万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7724794
• 关键词: Address; Bioinformatics; Biological; Biology; Biomedical Research; Computational Biology; Computer Simulation; Data; Development; Disease; Drug Design; Genetic; Goals; Human; Journals; Life; Literature; Methods; Mining; Performance; Protein Databases; Proteins; Publications; Research; Research Personnel; Site; Source; System; Techniques; Testing; Text; Time; Validation; Work; drug discovery; innovation; novel; protein function; research study; text searching

DESCRIPTION (provided by applicant): The function of millions of proteins remains unknown, and automated protein function prediction systems have a poor record of performance. We will test hypotheses about protein functional sites by validating high-throughput predictions derived from computational biology techniques through a novel automated system that will mine the literature for targeted information relevant to those predictions. The impact of our work will be to enable large-scale, validated, annotation of protein function and in turn to facilitate progress in tackling drug discovery for treatment of diseases. High-throughput experiments and bioinformatics techniques are creating an exploding volume of data with which we hope to transcribe the genetic blueprints of life. Targeted experiments are required to validate biomedical discoveries from these sources. Fortunately, the information to confirm or refute a prediction is often already available in an existing publication and the biologist can take advantage of this supporting evidence for validation. However, the sheer volume of predictions from high throughput methods exceeds the capacity of researchers to perform even the necessary literature searches. This gap in capacity must be addressed using automated literature mining methods that perform comparably to a human expert; indeed, development of such methods is a grand challenge of modern Biology. We will mine the full text literature to validate computational predictions of functional sites in proteins. The innovations in our approach include: (1) using computational predictions as the context for a literature search; (2) information extraction of protein functional sites from full text journal publications; (3) high-throughput text mining; and (4) using primary information in protein databases to evaluate the methods. Understanding of protein function is a critical bottleneck in the progress of biomedical research. It is time to truly integrate the biological literature into the protein function prediction problem. By doing so, we will enable a critical advance in high-throughput protein function prediction

描述（由申请人提供）：数百万蛋白质的功能仍然未知，自动蛋白质功能预测系统的性能记录不佳。我们将通过一个新的自动化系统验证来自计算生物学技术的高通量预测来测试有关蛋白质功能位点的假设，该系统将挖掘文献中与这些预测相关的目标信息。我们工作的影响将是实现大规模的、经过验证的蛋白质功能注释，从而促进疾病治疗药物发现的进展。 高通量实验和生物信息学技术正在创造大量爆炸性的数据，我们希望用这些数据来转录生命的遗传蓝图。需要有针对性的实验来验证这些来源的生物医学发现。幸运的是，证实或反驳预测的信息通常已经在现有的出版物中提供，生物学家可以利用这些支持证据进行验证。然而，高通量方法的预测量超过了研究人员进行必要文献检索的能力。这种能力上的差距必须使用自动化的文献挖掘方法来解决，这些方法对人类专家来说是一个巨大的挑战。 我们将挖掘全文文献，以验证蛋白质中功能位点的计算预测。我们方法的创新包括：（1）使用计算预测作为文献检索的背景;（2）从全文期刊出版物中提取蛋白质功能位点的信息;（3）高通量文本挖掘;（4）使用蛋白质数据库中的主要信息来评估方法。 对蛋白质功能的理解是生物医学研究进展中的一个关键瓶颈。现在是时候将生物学文献真正整合到蛋白质功能预测问题中了。通过这样做，我们将在高通量蛋白质功能预测方面取得关键进展