Automated Literature Mining for Validation of High-Throughput Function Prediction

描述(申请人提供)：数百万种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很差。我们将通过一种新型的自动化系统来验证来自计算生物学技术的高通量预测，该系统将从文献中挖掘与这些预测相关的目标信息，从而测试关于蛋白质功能位点的假设。我们工作的影响将是实现对蛋白质功能的大规模、有效的注释，并反过来促进在治疗疾病的药物发现方面取得进展。 高通量实验和生物信息学技术正在创造爆炸性的数据量，我们希望用这些数据转录生命的基因蓝图。为了验证来自这些来源的生物医学发现，需要进行有针对性的实验。幸运的是，证实或驳斥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，来自高通量方法的预测的绝对数量超过了研究人员执行必要的文献搜索的能力。这种能力上的差距必须用自动化的文献挖掘方法来解决，这种方法的表现可以与人类专家相媲美；事实上，这种方法的发展是现代生物学的一个巨大挑战。 我们将挖掘全文文献来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搜索的上下文；(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；以及(4)使用蛋白质数据库中的原始信息来评估方法。 对蛋白质功能的了解是生物医学研究进展中的一个重要瓶颈。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重大进展。

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