A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

ABSTRACT:

Background

Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to transform multi-omics data into latent representations. One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late. The literature on integration methods is growing steadily, however, close to nothing is known about the relative performance of these methods under fair experimental conditions and under consideration of different use cases.

Results

We developed a comparison framework that trains and optimizes mul

SUBMITTER: Hauptmann T 

PROVIDER: S-EPMC9926634 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature