Graph-based multi-modality integration for prediction of cancer subtype and severity.

ABSTRACT: Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train

SUBMITTER: Duroux D 

PROVIDER: S-EPMC10638406 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature