Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of conventional ...

A deep count autoencoder network to denoise scRNA-seq data and remove the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...

Abstract: Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to transfer a deep network trained by ...

Abstract: A stacked autoencoder (SAE) is a widely used deep network. However, existing deep SAEs focus on original samples without considering the hierarchical structural information between samples.

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational ...

This model is able to accurately deconvolve bulk RNA-seq data into cell fractions and predict cell-type-specific gene expression at cell-type level based on scRNA-seq data. related article Deep ...