A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network.

ABSTRACT:

Introduction

In recent years, lots of computational models have been proposed to infer potential lncRNA-disease associations.

Methods

In this manuscript, we introduced a novel end-to-end learning framework named CNMCLDA, in which, we first adopted two convolutional neural networks to extract hidden features of diseases and lncRNAs separately. And then, by combining these hidden features of diseases and lncRNAs with known lncRNA-disease associations, we designed five different loss functions. Next, based on errors obtained by these loss functions, we would perform back propagation to fit parameters in CNMCLDA, and complete those missing values in lncRNA-disease relational matrix according to these fitted parameters. In order to demonstrate the prediction performance of CNMCLD

SUBMITTER: Zhou S 

PROVIDER: S-EPMC12014579 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature