AsmbPLS: biomarker identification and patient survival prediction with multi-omics data.

ABSTRACT:

Introduction

With the advancement of high-throughput studies, an increasing wealth of high-dimensional multi-omics data is being collected from the same patient cohort. However, leveraging this multi-omics data to predict survival outcomes poses a significant challenge due to its complex structure.

Methods

In this article, we present a novel approach, the Adaptive Sparse Multi-Block Partial Least Squares (asmbPLS) Regression model, which introduces a dynamic assignment of penalty factors to distinct blocks within various PLS components, facilitating effective feature selection and prediction.

Results

We compared the proposed method with several state-of-the-art algorithms encompassing prediction performance, feature selection and computation efficiency. We conducted c

SUBMITTER: Zhang R 

PROVIDER: S-EPMC11621212 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature