Work

A general kernel boosting framework integrating pathways for predictive modeling based on genomic data

Year: 2022

Type: conference-paper

Abstract: In this article, we extend a general framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical information and prior knowledge about pathways for prediction of binary, continuous and survival outcomes. We introduce appropriate loss functions and optimization procedures for different outcome types. Our prediction algorithm incorporates pathway knowledge by constructing kernel function spaces from the pathways and use them as base learners in the boosting procedure. Through extensive simulations and case studies in drug response and cancer survival datasets, we demonstrate that PKB can substantially outperform other competing methods, better identify biological pathways related to drug response and patient survival, and provide novel insights into cancer pathogenesis and treatment response.

Source: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

DOI: 10.1145/3535508.3545526

Authors: Li Zeng, Zhaolong Yu, Yiliang Zhang, Hongyu Zhao

Institution: Yale University

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: National Institutes of Health