A benchmark study of deep learning-based multi-omics data fusion methods for cancer.

ABSTRACT:

Background

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.

Results

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 perform

SUBMITTER: Leng D 

PROVIDER: S-EPMC9361561 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature