Abstract
Over the last decade, regularized regression methods have offered alternatives for performing multi-marker analysis and feature selection in a whole genome context. The process of defining a list of genes that will characterize an expression profile remains unclear. It currently relies upon advanced statistics and can use an agnostic point of view or include some a priori knowledge, but overfitting remains a problem. This paper introduces a methodology to deal with the variable selection and model estimation problems in the high-dimensional set-up, which can be particularly useful in the whole genome context. Results are validated using simulated data and a real dataset from a triple-negative breast cancer study.
Highlights
Introduction25% of all new cancer diagnoses in women [1]
Breast cancer (BC) is the most frequent cancer among women, representing around25% of all new cancer diagnoses in women [1]
The worst outcomes are associated with the so-called triple-negative breast cancer subtype (TNBC), diagnosed in 15–20% of BC
Summary
25% of all new cancer diagnoses in women [1]. One in eight women in developed countries will be diagnosed with BC over the course of a lifetime. The prognosis of this disease has progressively improved over the past three decades, due to the implementation of population-based screening campaigns and, above all, the introduction of new effective targeted medical therapies, i.e., aromatase inhibitors (effective in hormone receptor-positive tumors) and trastuzumab (effective in HER2-positive tumors). The worst outcomes are associated with the so-called triple-negative breast cancer subtype (TNBC), diagnosed in 15–20% of BC patients. The absence of expression of these receptors makes chemotherapy the only available therapy for TNBC
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
