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Journal ArticleDOI

Minimum redundancy feature selection from microarray gene expression data.

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TLDR
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes.
Abstract: 
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their ...

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Proceedings ArticleDOI

Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units.

TL;DR: Experimental results show that the proposed data-driven methodology outperforms traditional approaches and yields better results based on the evaluation of real-world ICU data from 4000 subjects in the database, showing great potentials for the use of data- driven analytics to improve the quality of healthcare services.
Proceedings ArticleDOI

RaPtomics: Integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer

TL;DR: The RaPtomic prognostic model using Linear Discriminant Analysis (LDA) classifier, in conjunction with two radiomic and two pathomic shape features, significantly predicted 5-year recurrence free survival (RFS) as compared to radiomic (AUC 0.74; p<0.01) and pathomic and path genomic features alone.
Journal ArticleDOI

High-dimensional supervised feature selection via optimized kernel mutual information

TL;DR: The OKMI method solves the problem of computation complexity in the probability of distribution, and avoids this problem by finding the optimal features at very low computational cost and is effective and robust over a wide range of real applications on expert systems.
Journal ArticleDOI

Feature selection based on measurement of ability to classify subproblems

TL;DR: Two algorithms are designed for progressively selecting features, by firstly eliminating irrelevant features and then abandoning redundant features based on discrimination structure complementarity, and Experimental results demonstrate the effectiveness of the proposed method.
Journal ArticleDOI

CWLy-SVM: A support vector machine-based tool for identifying cell wall lytic enzymes.

TL;DR: A support vector machine-based cell wall lytic enzyme identification model was constructed using bioinformatics and comprehensively analyzed the selected optimal features and used the proposed model to construct a user friendly web server called the CWLy-SVM to identify cell wall Lytic enzymes.
References
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Journal ArticleDOI

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Journal ArticleDOI

Wrappers for feature subset selection

Ron Kohavi, +1 more
- 01 Dec 1997 - 
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
Journal ArticleDOI

A comparison of methods for multiclass support vector machines

TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Journal ArticleDOI

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.

TL;DR: In this paper, a two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.
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