人类遗传学
水准点(测量)
基因组生物学
计算生物学
计算机科学
深度学习
人工智能
基因组学
基因组
生物信息学
遗传学
生物
基因
大地测量学
地理
作者
Dongjin Leng,Linyi Zheng,Yao Wen,Yunhao Zhang,Lianlian Wu,Jing Wang,Meihong Wang,Zhongnan Zhang,Song He,Xiaochen Bo
出处
期刊:Genome Biology
[Springer Nature]
日期:2022-08-09
卷期号:23 (1)
被引量:39
标识
DOI:10.1186/s13059-022-02739-2
摘要
A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples.In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks.Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo .
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