Classification Algorithm for High Dimensional Protein Markers in Time-course Data

TL;DR

This paper presents a statistical method combining Cox models and Bayesian techniques to classify protein biomarkers in high-dimensional, time-course cancer data, aiding in identifying relevant biomarkers for prognosis.

Contribution

Develops an efficient, integrated statistical framework using joint modeling and Bayesian methods for classifying high-dimensional protein markers in cancer progression.

Findings

Optimal thresholds for markers identified using Cox models.

Validation of classification through frailty models.

Prognostic score developed for patient outcome prediction.

Abstract

Identification of biomarkers is an emerging area in Oncology. In this article, we develop an efficient statistical procedure for classification of protein markers according to their effect on cancer progression. A high-dimensional time-course dataset of protein markers for 80 patients motivates us for developing the model. We obtain the optimal threshold values for markers using Cox proportional hazard model. The optimal threshold value is defined as a level of a marker having maximum impact on cancer progression. The classification was validated by comparing random components using both proportional hazard and accelerated failure time frailty models. The study elucidates the application of two separate joint modeling techniques using auto regressive-type model and mixed effect model for time-course data and proportional hazard model for survival data with proper utilization of Bayesian methodology. Also, a prognostic score has been developed on the basis of few selected genes with application on patients. The complete analysis is performed by R programming code. This study facilitates to identify relevant biomarkers from a set of markers.