时间轴
背景(考古学)
数据科学
大数据
计算机科学
药物发现
公共领域
领域(数学分析)
药物开发
医学
药品
生物信息学
数据挖掘
药理学
生物
历史
古生物学
数学分析
数学
考古
作者
Nathan Brown,Jean Cambruzzi,Peter Cox,Mark Davies,James B. Dunbar,Dean Plumbley,Matthew A Sellwood,Aaron Sim,Bryn Williams–Jones,Magdalena Zwierzyna,David W. Sheppard
出处
期刊:Progress in Medicinal Chemistry
日期:2018-01-01
卷期号:: 277-356
被引量:39
标识
DOI:10.1016/bs.pmch.2017.12.003
摘要
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
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