Lüscher-Dias Thomaz, Siqueira Dalmolin Rodrigo Juliani, de Paiva Amaral Paulo, Alves Tiago Lubiana, Schuch Viviane, Franco Glória Regina, Nakaya Helder I
Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
Bioinformatics Multidisciplinary Environment-BioME, IMD, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
iScience. 2021 Dec 11;25(1):103610. doi: 10.1016/j.isci.2021.103610. eCollection 2022 Jan 21.
Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.
每周都有成千上万篇生物医学科学文章发表，其中包括那些描述与人类疾病相关基因的文章。诸如文本挖掘和机器学习算法等计算方法现在能够自动检测这些关联。在本研究中，我们使用了一种认知计算文本挖掘应用程序来构建一个包含3723个基因和99种疾病的知识网络。然后，我们追踪这些网络的年度变化，以分析我们的知识在过去30年中是如何演变的。我们的系统方法有助于揭示疾病的分子基础，并检测临床上不同疾病之间的共同机制。它还表明多用途治疗药物靶向的基因通常与几种精神、炎症或感染性疾病相关。通过驾驭这一知识洪流，我们能够提取有关人类疾病的相关生物学信息和见解。
Zotero 插件
