Deep Learning for Biomarker Discovery in Cancer Genomes.

ABSTRACT:

Background

Genomic data is essential for clinical decision-making in precision oncology. Bioinformatic algorithms are widely used to analyze next-generation sequencing (NGS) data, but they face two major challenges. First, these pipelines are highly complex, involving multiple steps and the integration of various tools. Second, they generate features that are human-interpretable but often result in information loss by focusing only on predefined genetic properties. This limitation restricts the full potential of NGS data in biomarker extraction and slows the discovery of new biomarkers in precision oncology.

Methods

We propose an end-to-end deep learning (DL) approach for analyzing NGS data. Specifically, we developed a multiple instance learning DL framework that integrates

SUBMITTER: Unger M 

PROVIDER: S-EPMC11741323 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature