Computers in Biology and Medicine, ( ISI ), Volume (70), No (1), Year (2016-1) , Pages (58-66)

Title : ( A Robust aCGH Data Recovery Framework Based on Half Quadratic Minimization )

Authors: Majeed Mohammadi , Ghosheh Abed Hodtani ,

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This paper presents a general half quadratic framework for simultaneous analysis of the whole array com- parative genomic hybridization (aCGH) profiles in a data set. The proposed framework accommodates different M-estimation loss functions and two underlying assumptions for aCGH profiles of a data set: sparsity and low rank. Using M-estimation loss functions, this framework is more robust to various types of noise and outliers. The solution of the proposed framework is given by half quadratic (HQ) mini- mization. To hasten this procedure, accelerated proximal gradient (APG) is utilized. Experimental results support the robustness of the proposed framework in comparison to the state-of-the-art algorithms.

Keywords

, Cancer, CNV, aCGH, half quadratic,
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@article{paperid:1054067,
author = {Mohammadi, Majeed and Abed Hodtani, Ghosheh},
title = {A Robust aCGH Data Recovery Framework Based on Half Quadratic Minimization},
journal = {Computers in Biology and Medicine},
year = {2016},
volume = {70},
number = {1},
month = {January},
issn = {0010-4825},
pages = {58--66},
numpages = {8},
keywords = {Cancer; CNV; aCGH; half quadratic; Correntropy},
}

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%0 Journal Article
%T A Robust aCGH Data Recovery Framework Based on Half Quadratic Minimization
%A Mohammadi, Majeed
%A Abed Hodtani, Ghosheh
%J Computers in Biology and Medicine
%@ 0010-4825
%D 2016

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