Tari Luis, Anwar Saadat, Liang Shanshan, Hakenberg Jörg, Baral Chitta
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA.
Pac Symp Biocomput. 2010:465-76. doi: 10.1142/9789814295291_0048.
Biological pathways are seen as highly critical in our understanding of the mechanism of biological functions. To collect information about pathways, manual curation has been the most popular method. However, pathway annotation is regarded as heavily time-consuming, as it requires expert curators to identify and collect information from different sources. Even with the pieces of biological facts and interactions collected from various sources, curators have to apply their biological knowledge to arrange the acquired interactions in such a way that together they perform a common biological function as a pathway. In this paper, we propose a novel approach for automated pathway synthesis that acquires facts from hand-curated knowledge bases. To comprehend the incompleteness of the knowledge bases, our approach also obtains facts through automated extraction from Medline abstracts. An essential component of our approach is to apply logical reasoning to the acquired facts based on the biological knowledge about pathways. By representing such biological knowledge, the reasoning component is capable of assigning ordering to the acquired facts and interactions that is necessary for pathway synthesis. We demonstrate the feasibility of our approach with the development of a system that synthesizes pharmacokinetic pathways. We evaluate our approach by reconstructing the existing pharmacokinetic pathways available in PharmGKB. Our results show that not only that our approach is capable of synthesizing these pathways but also uncovering information that is not available in the manually annotated pathways.
生物途径在我们理解生物功能机制方面被视为至关重要。为了收集有关途径的信息，人工编目一直是最常用的方法。然而，途径注释被认为非常耗时，因为它需要专业编目人员从不同来源识别和收集信息。即使从各种来源收集了生物事实和相互作用的片段，编目人员也必须运用他们的生物学知识，以一种使这些相互作用共同作为一个途径执行共同生物功能的方式来安排所获取的相互作用。在本文中，我们提出了一种新的自动途径合成方法，该方法从人工整理的知识库中获取事实。为了理解知识库的不完整性，我们的方法还通过从Medline摘要中自动提取来获取事实。我们方法的一个重要组成部分是基于关于途径的生物学知识对所获取的事实应用逻辑推理。通过表示这样的生物学知识，推理组件能够为途径合成所需的所获取的事实和相互作用分配顺序。我们通过开发一个合成药代动力学途径的系统来证明我们方法的可行性。我们通过重建PharmGKB中现有的药代动力学途径来评估我们的方法。我们的结果表明，我们的方法不仅能够合成这些途径，而且还能揭示手动注释途径中没有的信息。
