Towards the discovery of reliable biomarkers from gene-expression profiles: an iterative constraint satisfaction learning approach

Authors:
George Potamias;Lefteris Koumakis;Alexandros Kanterakis;Vassilis Moustakis
Affiliations:
Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece;Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece;Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece;Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece
Venue:
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Year:
2010

Citing 9
Cited 0

A Framework for Average Case Analysis of Conjunctive Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Implementing deductive databases by mixed integer programming

ACM Transactions on Database Systems (TODS)
The Power of Decision Tables

ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Microarray data mining: facing the challenges

ACM SIGKDD Explorations Newsletter
Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

Journal of Biomedical Informatics
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter

Quantified Score

Hi-index	0.00

Visualization

Abstract

The article demonstrates the use of Multiple Iterative Constraint Satisfaction Learning (MICSL) process in inducing gene-markers from microarray gene-expression profiles MICSL adopts a supervised learning from examples framework and proceeds by optimizing an evolving zero-one optimization model with constraints After a data discretization pre-processing step, each example sample is transformed into a corresponding constraint Extra constraints are added to guarantee mutual-exclusiveness between gene (feature) and assigned phenotype (class) values The objective function corresponds to the learning outcome and strives to minimize use of genes by following an iterative constraint-satisfaction mode that finds solutions of increasing complexity Standard (c4.5-like) pruning and rule-simplification processes are also incorporated MICSL is applied on several well-known microarray datasets and exhibits very good performance that outperforms other established algorithms, providing evidence that the approach is suited for the discovery of biomarkers from microarray experiments Implications of the approach in the biomedical informatics domain are also discussed.