Chemical Pattern Detection and Visualization in Biological Networks

描述（由申请人提供）：虽然大量的分子生物活性数据创造了新的机会，但由于处理许多独立和异构数据源的固有困难，它也阻碍了科学家进行生物医学研究的方式。数据输入的质量和类型往往限制了项目成果。为了提高研究成果，获得所有可用的数据和多个备择假设检验是必不可少的。针对经验不足的最终用户，我们将开发工具，促进小分子/生物活性/生物医学数据领域的“跳跃”，从一个潜在的解决方案到另一个，鼓励用户探索多个替代假设。我们将从多个生物活性数据库中整合数据，包括PubChem，ChemBank，ChEMBL，PDSP和WOMBAT，到一个集中的系统中。我们将开发先进的化学模式识别算法，并提供基于Cytoscape的可视化工具，用于全球探索化学模式与生物活性/目标之间的关系。我们将通过三个具体目标来实现这一目标：1。为许多异构数据库创建一个简单的统一界面，卡尔斯巴德（小分子生物活性数据联合注释研究库）;数据将协调多个来源的人，大鼠和小鼠靶标的小分子生物活性数据。2.开发用于化学模式检测和注释的高级算法;我们将检测最大重叠集（MOS）和HierS（分层支架），并相应地在CARSLBAD中注释化学品。3.开发一个Cytoscape插件，用于化学模式生物活性网络的可视化和探索。通过MOS/HierS模式，用户将能够识别目标特定的化学特征（活性和选择性的决定因素）;在没有特定信号的情况下，这些模式将作为脱靶和混杂生物活性预测的依据。卡尔斯巴德存储独特的靶向配体生物活性数据以及化学模式，将在企业平台上设计、实施和维护，供科学界使用。新的Cytoscape插件将与现有的核心组件和插件集成，以多学科的方式跨越化学和生物学。 公共卫生相关性：这项研究旨在为化学和生物学研究界提供一种创新的、基于网络的工具，用于挖掘大量的化学和生物学数据。它将为研究人员提供一种有效和改进的方式，以多学科的方式评估，可视化和探索小分子生物活性数据，从而提高人类健康研究的产出。

DESCRIPTION (provided by applicant): While the massive amount of molecular bioactivity data creates new opportunities, it also hinders the way scientists conduct biomedical research due to the inherent difficulty of processing many separate and heterogeneous data sources. The quality and type of data input often limits the project outcome. To improve research outcome, access to all available data and multiple alternative hypothesis testing are essential. Targeting less experienced end-users, we will develop tools that facilitate "jumps" in the small molecule / bioactivity / biomedical data area, leading from one potential solution to another, encouraging users to explore multiple, alternate hypotheses. We will integrate data from multiple bioactivity databases, including PubChem, ChemBank, ChEMBL, PDSP and WOMBAT, into one centralized system. We will develop advanced chemical pattern recognition algorithms and deliver a Cytoscape-based visualization tool for the global exploration of relationships between chemical patterns and biological activities/targets. We will achieve this via three Specific Aims: 1. Create one simple unified interface for many heterogeneous databases, CARLSBAD (Confederated Annotated Research Libraries of Small molecule Biological Activity Data); the data will reconcile small molecule bioactivity data across multiple sources for human, rat and mouse targets. 2. Develop advanced algorithms for chemical pattern detection and annotation; we will detect the Maximum Overlapping Set (MOS) and HierS (hierarchical scaffolds) and annotate chemicals in CARSLBAD accordingly. 3. Develop a Cytoscape plugin for the visualization and exploration of chemical pattern bioactivity networks. Via MOS/HierS patterns, users will be able to identify target specific chemical signatures (determinants for activity and selectivity); in the absence of specific signals, these patterns will serve as rationale for off- target and promiscuous bioactivity prediction. Storing unique target-ligand bioactivity data as well as chemical patterns, CARLSBAD will be designed, implemented and maintained on an enterprise platform for use by the scientific community. The new Cytoscape plugin will integrate with existing core components and plugins to bridge across chemistry and biology in a multi-disciplinary manner. PUBLIC HEALTH RELEVANCE: The proposed research aims to empower the chemistry and biology research community with an innovative, network-based tool for mining vast amounts of chemical and biological data. It will provide an effective and improved way for researchers to evaluate, visualize and explore small molecule bioactivity data in a multi-disciplinary manner, thus leading to improved output in human health research.