ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation.

ABSTRACT:

Introduction

The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and conditions. However, the complexity and volume of RNA-Seq data pose challenges in identifying differentially expressed genes (DEGs), critical for understanding the molecular basis of diseases like cancer.

Methods

We introduce a novel Machine Learning-Enhanced Genomic Data Analysis Pipeline (ML-GAP) that incorporates autoencoders and innovative data augmentation strategies, notably the MixUp method, to overcome these challenges. By creating synthetic training examples through a linear combination of input pairs and their labels, MixUp significantly enhances the model's abilit

SUBMITTER: Agraz M 

PROVIDER: S-EPMC11467662 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature