A review of supervised machine learning applied to ageing research.
A review of supervised machine learning applied to ageing research.
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DOI：
10.1007/s10522-017-9683-y
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发表时间：
2017-04
期刊：
影响因子：
4.5
通讯作者：
Freitas AA
中科院分区：
文献类型：
--
作者：
Fabris F;Magalhães JP;Freitas AA
从广义上讲，监督机器学习是学习注释数据（训练集）中变量之间的相关性的计算任务，并使用这些信息创建能够推断新数据注释的预测模型，其注释未知。衰老是一个复杂的过程，几乎影响所有动物物种。这个过程可以在几个抽象层次上进行研究，在不同的生物体中，并考虑到不同的目标。毫不奇怪，用于回答生物学问题的监督机器学习算法的多样性反映了正在研究的潜在衰老过程的复杂性。最近发表了许多使用监督机器学习来研究衰老过程的作品，因此现在是时候回顾这些作品，讨论它们的主要发现和弱点。总之，综述论文的主要发现是：特定类型的DNA修复与衰老之间的联系;衰老相关蛋白往往高度相关，似乎在分子途径中发挥核心作用;衰老/长寿与自噬和凋亡、营养受体基因以及铜和铁离子转运有关。此外，通过机器学习发现了几种衰老的生物标志物。尽管有一些有趣的机器学习结果，但我们也发现了当前关于这一主题的工作的一个弱点：只有一篇评论论文通过湿实验室实验证实了机器学习算法的计算结果。总之，有监督的机器学习有助于推进我们的知识，并提供了关于衰老的新见解，但未来的工作应该更加重视验证预测。
Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.