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基于生物医学文献挖掘和通路分析的转录组学数据和代谢组学数据的整合。

Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis.

机构信息

R&D Division, Eriks-Precision Components India Pvt Ltd, Mohali, Punjab, India.

出版信息

Methods Mol Biol. 2022;2496:301-316. doi: 10.1007/978-1-0716-2305-3_16.

Abstract

Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.

摘要

组学技术(如转录组学和代谢组学)的最新进展为使用生物医学文献挖掘来理解疾病机制和确定相关的生物医学实体提供了前所未有的机会。生物医学文献中大量可用的数据有助于解决复杂的生物医学问题。基因组学和转录组学的进步有助于解码从各种高通量技术获得的遗传信息,以便将其用于个性化医疗和治疗。将生物医学文献中的数据与大规模基因组研究的数据相结合,有助于确定疾病的病因和药物靶点。本章介绍了转录组学和代谢组学在生物医学文献挖掘中的观点,并概述了该领域的最新技术。

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