AUTHOR=Zhang Runzhi , Datta Susmita 

TITLE=asmbPLS: biomarker identification and patient survival prediction with multi-omics data

JOURNAL=Frontiers in Genetics

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1444054

DOI=10.3389/fgene.2024.1444054

ISSN=1664-8021

ABSTRACT=IntroductionWith 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.MethodsIn 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.ResultsWe compared the proposed method with several state-of-the-art algorithms encompassing prediction performance, feature selection and computation efficiency. We conducted comprehensive evaluations using both simulated data with various scenarios and a real dataset from the melanoma patients to validate the effectiveness and efficiency of the asmbPLS method. Additionally, we applied the lung squamous cell carcinoma (LUSC) dataset from The Cancer Genome Atlas (TCGA) to further assess the feature selection capability of asmbPLS.DiscussionThe inherent nature of asmbPLS imparts it with higher sensitivity in feature selection compared to other methods. Furthermore, an R package called asmbPLS implementing this method is made publicly available.