Pareto analysis for gene filtering in microarray experiments

G. Fleury; A. Hero; S. Yoshida; T. Carter; C. Barlow; A. Swaroop

Pareto analysis for gene filtering in microarray experiments

G. Fleury, A. Hero, S. Yoshida, T. Carter, C. Barlow, A. Swaroop

Surgery

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12 Citations (Scopus)

Abstract

We introduce a method for detecting strongly monotone evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene filtering via multi-objective optimization to reveal genes which have the properties of: strong monotonic increase, high end-to-end slope and low slope deviation. Both a global Pareto optimization and a pair-wise local Pareto optimization are investigated. This gene filtering method is illustrated on mouse retinal genes acquired at different points over the lifetimes of a population of mice.

Original language	English
Article number	7072128
Journal	European Signal Processing Conference
Volume	2002-March
Publication status	Published - Mar 27 2002
Event	11th European Signal Processing Conference, EUSIPCO 2002 - Toulouse, France Duration: Sept 3 2002 → Sept 6 2002

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

Cite this

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abstract = "We introduce a method for detecting strongly monotone evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene filtering via multi-objective optimization to reveal genes which have the properties of: strong monotonic increase, high end-to-end slope and low slope deviation. Both a global Pareto optimization and a pair-wise local Pareto optimization are investigated. This gene filtering method is illustrated on mouse retinal genes acquired at different points over the lifetimes of a population of mice.",

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T1 - Pareto analysis for gene filtering in microarray experiments

AU - Fleury, G.

AU - Hero, A.

AU - Yoshida, S.

AU - Carter, T.

AU - Barlow, C.

AU - Swaroop, A.

PY - 2002/3/27

Y1 - 2002/3/27

N2 - We introduce a method for detecting strongly monotone evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene filtering via multi-objective optimization to reveal genes which have the properties of: strong monotonic increase, high end-to-end slope and low slope deviation. Both a global Pareto optimization and a pair-wise local Pareto optimization are investigated. This gene filtering method is illustrated on mouse retinal genes acquired at different points over the lifetimes of a population of mice.

AB - We introduce a method for detecting strongly monotone evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene filtering via multi-objective optimization to reveal genes which have the properties of: strong monotonic increase, high end-to-end slope and low slope deviation. Both a global Pareto optimization and a pair-wise local Pareto optimization are investigated. This gene filtering method is illustrated on mouse retinal genes acquired at different points over the lifetimes of a population of mice.

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M3 - Conference article

AN - SCOPUS:84960871577

SN - 2219-5491

VL - 2002-March

JO - European Signal Processing Conference

JF - European Signal Processing Conference

M1 - 7072128

T2 - 11th European Signal Processing Conference, EUSIPCO 2002

Y2 - 3 September 2002 through 6 September 2002

ER -

Pareto analysis for gene filtering in microarray experiments

Abstract

All Science Journal Classification (ASJC) codes

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