Statistical signal processing for cancer stem cell formation

Mónica F. Bugallo; Petar M. Djurcí

doi:10.1007/978-3-642-30574-0_28

Statistical signal processing for cancer stem cell formation

Mónica F. Bugallo
, Petar M. Djurcí

Office of the Provost

Stony Brook University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Many mysteries related to the behavior of cancer stem cells (CSCs), their role in the formation of tumors, and the evolution of tumors with time remain unresolved. Biologists conduct experiments and collect data from them; these are then used for modeling, inference, and prediction of various unknowns that define the CSC system and are vital for its understanding. The aim of this chapter is to provide a summary of our progress in statistical signal processing models and methods for the advancement of the theory and understanding of cancer and the CSC paradigm. The chapter comprises three parts: model building, methods for the forward problem, and methods for the inverse problem.

Original language	English
Title of host publication	Springer Handbook of Bio-/Neuroinformatics
Publisher	Springer Berlin Heidelberg
Pages	465-475
Number of pages	11
ISBN (Electronic)	9783642305740
ISBN (Print)	9783642305733
DOIs	https://doi.org/10.1007/978-3-642-30574-0_28
State	Published - Jan 1 2014

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10.1007/978-3-642-30574-0_28

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Statistical signal processing for cancer stem cell formation

Abstract

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