Paper Info
Title
Two-Variate Phenotype-Targeted Tests For Detecting Phenotypic Biomarkers In Cancers
Abstract
Detection of cancer-related phenotypic biomarkers is crucial for clinical research. Traditional pipeline consists of two stages, i.e., candidates are first selected to be significantly differentially expressed between tumour-adjacent and tumour conditions, and then later are filtered by Phenotype-Targeted tests (PT tests). Such two-phase process has low-detection power. In this paper, two-variate PT test, which jointly considers tumour-adjacent data and tumour data, is adopted to strengthen the detection power. We conduct a systematic investigation on the three implementations of two-variate PT tests for detecting phenotypic biomarkers in three types of cancers, and provide a practical guideline for the usage of the two-variate PT tests. Experimental analysis indicates that the two-variate PT tests achieve stronger detection power than traditional methods. The tumour-adjacent data provides complementary information to the discriminant analysis, and Fisher discriminant analysis is able to best implement two-variate PT test for detecting phenotypic biomarkers in cancers.
Year
DOI
Venue
2020
10.1504/IJDMB.2020.109501
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
Keywords
DocType
Volume
two-variate phenotype-targeted test, phenotypic biomarkers, breast cancer, lung cancer, thyroid cancer, body mass index, overall survival time, pathologic stage, microarray expression data, RNA-seq expression data
Journal
24
Issue
ISSN
Citations 
1
1748-5673
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jin-Xiong Lv101.69
Shikui Tu23914.25
Lei Xu33590387.32