A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies.

ABSTRACT:

Motivation

Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usually involves intrusive procedures. To gain valuable insights into disease mechanisms, it is thus essential to improve the quality of exmiR expression data and develop noninvasive methods for assessing intracellular mRNA expression.

Results

We developed CrossPred, a deep-learning multi-encoder model for the cross-prediction of exmiRs and mRNAs. Utilizing contrastive learning, we created a shared embedding space to integrate exmiRs and mRNAs. This shared embedding was then used to predict intracellular mRNA expression from noisy ex

SUBMITTER: Athaya T 

PROVIDER: S-EPMC11565234 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature