Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem be...Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions.The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data.Accurate disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical practice.We propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data.Our model is built upon a simple baseline,and we modified it for genomic data.Supportbased initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce over-fitting.Moreover,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples.Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.展开更多
目的:对亚健康中医证型分布规律及特点进行文献研究和系统评价。方法:计算机检索中国期刊全文数据库(CNKI)、万方(Wanfang)、维普(VIP)、中国生物医学文献服务系统(SinoMed)、Pubmed、Web of science、EMbase、Scopus和Cochrane Librar...目的:对亚健康中医证型分布规律及特点进行文献研究和系统评价。方法:计算机检索中国期刊全文数据库(CNKI)、万方(Wanfang)、维普(VIP)、中国生物医学文献服务系统(SinoMed)、Pubmed、Web of science、EMbase、Scopus和Cochrane Library建库至2023年2月8日有关亚健康中医证型的研究文献,经逐层筛选文献、质量评价后,采用Excel整理亚健康中医证型的频数分布,Stata软件进行Meta分析。应用SPSS软件,采用层次聚类Ward's Method法进行单证证型聚类分析。结果:(1)共纳入22个研究,涉及证型129种,包括14690例患者;(2)频数分布前5种的证型为肝郁脾虚证、肝肾阴虚证、肝郁气滞证、心脾两虚证和肝郁化火证;(3)以肝肾阴虚证为例,Meta分析结果显示证型比例为8%,95%CI(5%,11%);(4)将前19种复合证型进行两两拆分归纳后,共得到7种单证证型,聚类分析后主要聚成3类。肝郁为第1类,脾虚和阴虚为第2类,气虚、痰热、阳虚和血虚为第3类。结论:亚健康主要证型为肝郁脾虚证、肝肾阴虚证、肝郁气滞证、心脾两虚证。肝郁是亚健康患者出现各类症状的主要因素。研究亚健康的证型分布频率和比例为临床诊疗提供了参考,也为亚健康的中医指南修订提供了循证证据。展开更多
基金supported by the Macao Science and Technology Development Funds Grands No.0158/2019/A3 from the Macao Special Administrative Region of the People’s Republic of China.
文摘Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions.The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data.Accurate disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical practice.We propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data.Our model is built upon a simple baseline,and we modified it for genomic data.Supportbased initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce over-fitting.Moreover,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples.Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.
文摘目的:对亚健康中医证型分布规律及特点进行文献研究和系统评价。方法:计算机检索中国期刊全文数据库(CNKI)、万方(Wanfang)、维普(VIP)、中国生物医学文献服务系统(SinoMed)、Pubmed、Web of science、EMbase、Scopus和Cochrane Library建库至2023年2月8日有关亚健康中医证型的研究文献,经逐层筛选文献、质量评价后,采用Excel整理亚健康中医证型的频数分布,Stata软件进行Meta分析。应用SPSS软件,采用层次聚类Ward's Method法进行单证证型聚类分析。结果:(1)共纳入22个研究,涉及证型129种,包括14690例患者;(2)频数分布前5种的证型为肝郁脾虚证、肝肾阴虚证、肝郁气滞证、心脾两虚证和肝郁化火证;(3)以肝肾阴虚证为例,Meta分析结果显示证型比例为8%,95%CI(5%,11%);(4)将前19种复合证型进行两两拆分归纳后,共得到7种单证证型,聚类分析后主要聚成3类。肝郁为第1类,脾虚和阴虚为第2类,气虚、痰热、阳虚和血虚为第3类。结论:亚健康主要证型为肝郁脾虚证、肝肾阴虚证、肝郁气滞证、心脾两虚证。肝郁是亚健康患者出现各类症状的主要因素。研究亚健康的证型分布频率和比例为临床诊疗提供了参考,也为亚健康的中医指南修订提供了循证证据。
