Losko Sascha, Heumann Klaus
Biomax Informatics AG, Robert-Koch-Str. 2, 82152, Planegg, Germany.
Methods Mol Biol. 2017;1613:403-423. doi: 10.1007/978-1-4939-7027-8_16.
The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data, including experimental data, originating from a multitude of "-omics" platforms, phenotype information, and clinical data. For bioinformatics, the challenge remains to structure this information so that scientists can identify relevant information, to integrate this information as specific "knowledge bases," and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation. Here we report on progress made in building a generic knowledge management environment capable of representing and mining both explicit and implicit knowledge and, thus, generating new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM™ Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.
学术和产业研究团队产生的大量信息反映在迅速增长的科学文献以及呈指数级扩展的形式化数据资源中，这些数据包括源自众多“组学”平台的实验数据、表型信息和临床数据。对于生物信息学而言，挑战依然存在，即如何构建这些信息，以便科学家能够识别相关信息，将这些信息整合为特定的“知识库”，并在多个科学领域将这些知识形式化，以促进假设的生成和验证。在此，我们报告在构建一个能够表示和挖掘显性与隐性知识从而生成新知识的通用知识管理环境方面所取得的进展。药物发现和临床研究中的风险管理被用作一个典型例子来说明这种方法。在本章中，我们介绍用于表示复杂生物医学网络的技术和概念（如实证本体、语义对象、类型化关系、上下文、图形和信息层）。以BioXM™知识管理环境为例，展示如何表示肿瘤学等领域以及如何将这种表示用于研究。
