Towards a robust out-of-the-box neural network model for genomic data.

ABSTRACT:

Background

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity.

Results

Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogene

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC8994362 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature