Journal Article10.1016/J.NEUCOM.2021.07.047
Gene selection for microarray data classification via dual latent representation learning
Xiao Zheng,Chujie Zhang +1 more
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TL;DR: This work proposes an effective method to select the most discriminative genes from high-dimensional microarray data for benefiting tumor classification and builds a novel computational model based on dual latent feature representation learning, referred as DLRL briefly, which can capture both the internal association of data samples and the relationship between different genes.
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About: This article is published in Neurocomputing. The article was published on 21 Oct 2021. The article focuses on the topics: Feature selection & Feature learning.
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Citations
A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis
TL;DR: A machine learning (ML) algorithm is proposed to diagnose different cancer diseases from big data with superior results in terms of the number of selected features and diagnostic accuracy.
Semi-supervised feature selection based on pairwise constraint-guided dual space latent representation learning and double sparse graphs discriminant
TL;DR: A novel semi-supervised feature selection algorithm called semi-supervised feature selection based on pairwise constraint-guided dual space latent representation learning and double sparse graphs discriminant (PCDLRD) is proposed and the viability of the proposed method and the effectiveness of the proposed method in classification tasks compared to the other six feature selection methods are validated.
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Predictive modelling for molecular cancer profile classification using hybrid learning techniques
TL;DR: A hybrid feature selection technique that combines RF (random forest) with PSO (particle swarm optimization) with PCA (principal component analysis) is suggested in this research work, and an empirical study reveals that the proposed hybrid approach yielded 98.77% accuracy.
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Laura J. van't Veer,Hongyue Dai,Marc J. van de Vijver,Yudong D. He,Augustinus A. M. Hart,Mao Mao,Hans Peterse,Karin van der Kooy,Matthew J. Marton,Anke T. Witteveen,George J. Schreiber,Ron M. Kerkhoven,Christopher J. Roberts,Peter S. Linsley,René Bernards,Stephen H. Friend +15 more
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Gene Selection for Cancer Classification using Support Vector Machines
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