Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Applied Survival Analysis: Regression Modeling of Time to Event Data
Applied Survival Analysis: Regression Modeling of Time to Event Data
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
A novel neural network-based survival analysis model
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Feature extraction and dimensionality reduction for mass spectrometry data
Computers in Biology and Medicine
Feature subset selection in large dimensionality domains
Pattern Recognition
Impact of censoring on learning Bayesian networks in survival modelling
Artificial Intelligence in Medicine
G-ANMI: A mutual information based genetic clustering algorithm for categorical data
Knowledge-Based Systems
Fuzzy rough set based attribute reduction for information systems with fuzzy decisions
Knowledge-Based Systems
Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking
Knowledge-Based Systems
Dimensionality reduction and main component extraction of mass spectrometry cancer data
Knowledge-Based Systems
Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network
Knowledge-Based Systems
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Artificial Intelligence in Medicine
Wavelet Analysis in Current Cancer Genome Research: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Biomarkers which predict patient's survival play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers of survival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to be significantly associated with survival time.