LINCS Information FramEwork (LIFE) to Integrate and Analyze Diverse Data Set

该申请寻求资金来开发LIFE -一种新型的信息系统，以无缝集成由NIH集成网络细胞签名库（LINCS）和其他筛选程序产生的各种数据集。LINCS计划旨在生成对各种小分子和遗传扰动的细胞反应特征的广泛参考集。其目标是创建一个可持续和广泛获取的知识资源，以促进我们对分子生物学成分在维持健康发展方面的高度协调的相互作用以及它们的扰动如何导致疾病的理解。LINCS产生的数据涵盖各种检测形式和技术，包括生物化学和单细胞表型反应，以及全基因组转录谱分析。这一举措的成功关键取决于一个有效的信息学解决方案，将各种（当前和未来）数据类型整合成连贯的数据集，并使其可供不同背景和不同目标的科学家访问、解释和操作。我们建议开发生命-一种新的知识为基础的 信息系统将解决这一挑战。在过去的十年中，语义Web技术的发展取得了巨大的进步，其目标是将知识形式化，将不同领域的信息联系起来，并集成来自不同来源的异构数据。LIFE将利用这些技术并进一步扩展它们。LIFE将整合生物医学领域级本体， 包括我们最近开发的BioAssay Ontology，以关联相关数据类型并提供LINCS测定及其结果的知识背景。LIFE在信息量和复杂性方面将是可扩展的。LIFE的一个关键的新特征是通过各种推理机制获得新的隐式知识的潜力;类似于人类如何通过（精神上）连接不同的信息来获得见解。LIFE系统的首要目标是帮助科学家 在自己的研究中使用LINCS和其他NIH筛选项目中产生的数据和结果，并支持其转化为新疗法的开发。

This application seeks funds to develop LIFE - a novel information system to seamlessly integrate diverse data set produced by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) and other screening programs. The LINCS program aims to generate an extensive reference set of cellular response signatures to a variety of small molecule and genetic perturbations. The goal is to create a sustainable and widely accessible knowledge resource to advance our understanding of the highly orchestrated interplay of molecular biological components in maintaining healthy development and how their perturbation causes disease. Data produced at LINCS span a variety of assay formats and technologies, including biochemical and single cell phenotypic responses, and genome-wide transcriptional profiling. The success of this initiative critically relies on an effective informatics solution to integrate the various (current and future) data types into coherent data sets and to make them accessible, interpretable, and actionable for scientists of different backgrounds and with different objectives. We propose to develop LIFE - a novel knowledge-based information system that will solve this challenge. Tremendous progress has been made during the last decade developing Semantic Web technologies with the goals of formalizing knowledge, linking information across different domains, and integrating heterogeneous data from diverse sources. LIFE will leverage these technologies and extend them further. LIFE will incorporate biomedical domain-level ontologies, including our recently developed BioAssay Ontology, to associate related data types and to provide a knowledge context of the LINCS assays and their outcomes. LIFE will be scalable with respect to information volume and complexity. A key novel feature of LIFE will be the potential to derive novel implicit knowledge by various inference mechanisms; similar to how humans obtain insights by (mentally) connecting different pieces of information. The overarching goal of the LIFE system is to help scientists to use data and results produced in the LINCS and other NIH screening programs in their own research and to support their translation towards the development of novel therapeutics.