Mansoor Saqi Data Science Institute, Imperial College London, UK.
Artem Lysenko Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Brief Bioinform. 2019 Mar 25;20(2):609-623. doi: 10.1093/bib/bby025.
Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.
大量源自分子医学实验的数据正在导致与疾病亚型相关的分子特征的鉴定。这些模式的语境化对于获得与疾病相关的异常过程的机制见解很重要，这通常涉及多种异构类型数据的整合。在这篇综述中，我们讨论了可以用于探索分子特征的生物学背景的知识表示，特别是三种主要方法，即途径映射方法、分子网络中心方法和将生物学陈述表示为知识图的方法。我们讨论了这些范例中的每一个的实用性，通过选择实际示例来说明如何利用它们，并确定该研究领域的持续挑战。
