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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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Citations
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Journal ArticleDOI

Key features for the characterization of Android malware families

TL;DR: Maximum relevance minimum redundancy and evolutionary algorithms guided by information correlation measures have been applied for feature selection on the well-known Android Malware Genome (Malgenome) dataset, achieving interesting results on the most informative features for the characterization of representative families of existing Android malware.
Journal ArticleDOI

PepFormer: End-to-End Transformer-Based Siamese Network to Predict and Enhance Peptide Detectability Based on Sequence Only.

TL;DR: Pepformer as discussed by the authors is a novel end-to-end Siamese network coupled with a hybrid architecture of a Transformer and gated recurrent units that is able to predict the peptide detectability based on peptide sequences only.
Journal ArticleDOI

Fuzzy-rough-neural-based f-information for gene selection and sample classification

TL;DR: The proposed Fuzzy-Rough-Neural-based f-Information (FRNf-I) is evaluated using ten gene expression datasets and simulation results show that the proposed approach compute f-information measure easily without discretisation.
Journal ArticleDOI

Multi-Objective Firefly Algorithm for Multi-Class Gene Selection

TL;DR: An experimental result shows that when compared to the existing method, there is less complexity, high classification accuracy of the proposed MFGS method.
Journal ArticleDOI

A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset

TL;DR: In this paper, the authors used a hybrid method to address the high dimensionality and imbalance of data, applying a Synthetic Minority Oversampling Technique and TomekLinks balancing approach to create balanced data and using Random Forest as the feature selecting measurement to reduce the dimensionality of data.
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

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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