Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.
BMC Bioinformatics. 2011 Aug 22;12:351. doi: 10.1186/1471-2105-12-351.
We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain.
The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology.
We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain Architecture Management System (BAMS).
我们旨在以可推广的形式整理已发表实验中的观测结果;通过推理这些观测结果来生成解释,然后查询经解释的知识以提供支持证据。我们在“BioScholar”项目(R01-GM083871)中提供了网络应用程序软件,该软件完全实现了这一过程,适用于一个定义明确的领域:使用示踪实验来研究大鼠大脑的神经连接。
这项工作的主要贡献是提供了一种称为“基于实验设计的知识工程”(KEfED)的实验观测知识表示形式的首次实例化,该形式基于实验变量及其相互依存关系。该软件有三个部分:(a)KEfED 模型编辑器 - 用于通过绘制实验方案流程图来创建 KEfED 模型的设计编辑器;(b)KEfED 数据接口 - 类似于电子表格的工具,允许用户输入特定模型的实验数据;(c)“神经连接矩阵”界面,以表示示踪数据解释的有序连接强度的表格形式呈现神经连接。该工具还允许用户查看特定连接的实验证据。BioScholar 是用 Flex 3.5 构建的。它使用 Persevere(一个 NoSQL 数据库)作为灵活的数据存储,并使用 PowerLoom®(一个成熟的一阶逻辑推理系统)执行使用 BAMS 神经解剖本体论进行空间推理的查询。
我们首先介绍 KEfED 方法作为一种通用方法,并描述其作为在新科学出版模型中的论证模型中引入结构化推理的一种方式的可能作用。然后,我们描述了我们的示例应用程序的设计和实现:BioScholar 软件。这被呈现为一个更大、更专业的生物信息学系统(Brain Architecture Management System,BAMS)的生物注释接口和补充推理工具包。