我们离用Lasso正确推断基因相互作用网络还远吗？
Are we far from correctly inferring gene interaction networks with Lasso?
摘要：
检测基因、SNP、蛋白质、代谢物等遗传化合物的相互作用，可以潜在地揭示复杂性状和常见遗传疾病背后的机制。在分析不同类型的遗传数据时考虑了几种方法，回归法是最广泛采用的方法之一。毫无疑问，基因表达谱是一种常见的数据类型，从基因表达谱中可以用不同的方法推断出基因调控网络。在这项工作中，我们回顾了九个惩罚回归方法应用于微阵列数据来推断网络的相互作用的拓扑结构。我们评估每种方法的生物数据的复杂性。我们分析了他们每个人的局限性，以建议一些预防措施，应考虑使他们的预测更显着和可靠。
英文摘要：
Detecting the interactions of genetic compounds like genes, SNPs, proteins, metabolites, etc. can potentially unravel the mechanisms behind complex traits and common genetic disorders. Several methods have been taken into consideration for the analysis of different types of genetic data, regression being one of the most widely adopted. Without any doubt, a common data type is represented by gene expression profiles, from which gene regulatory networks have been inferred with different approaches. In this work we review nine penalised regression methods applied to microarray data to infer the topology of the network of interactions. We evaluate each method with respect to the complexity of biological data. We analyse the limitations of each of them in order to suggest a number of precautions that should be considered to make their predictions more significant and reliable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
