Layman’s abstract for paper on classification algorithm for high‐dimensional protein markers in time‐course data

Each week, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Statistics in Medicine, with the full article now available to read in Early View here.

Vishwakarma, GKBhattacharjee, ABanerjee, SLiquet, BClassification algorithm for high‐dimensional protein markers in time‐course dataStatistics in Medicine20201– 17https://doi.org/10.1002/sim.8720

We developed an efficient statistical procedure for the 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. This study elucidates the application of two separate joint modelling 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 is developed based on a few selected genes with an application on patients. This study facilitates to identify relevant biomarkers from a set of markers.

 

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