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Hybrid arithmetic optimization algorithm with deep transfer learning based microarray gene expression classification model
Microarray gene expression (MGE) data classification is a vital challenge in biomedical and genomics research, designed to understand the difficult...
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Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise...
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Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional...
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A two-phase cuckoo search based approach for gene selection and deep learning classification of cancer disease using gene expression data with a novel fitness function
The early detection of cancer is of paramount importance in the medical field, as it can lead to more precise and effective interventions for...
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An efficient gene expression data classification using optimized bidirectional long short-term memory with self attention mechanism
In recent years, DNA microarray is a recent research technique aimed at classifying gene expression. This paper is to develop Optimal Bidirectional...
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Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction
Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases....
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Implicit Neural Representations for Joint Decomposition and Registration of Gene Expression Images in the Marmoset Brain
We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of...
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Improved gene expression diagnosis via cascade entropy-fisher score and ensemble classifiers
Feature selection is an important technique used in bioinformatics modeling to reduce the dimensionality of high-dimensional data. However,...
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Cancer prediction with gene expression profiling and differential evolution
In the field of bioinformatics, the classification of tumors is a difficult and time-consuming task. When diagnosing cancer, gene expression levels...
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Multiobjective Simultaneous Gene Ranking and Clustering
Microarray technology allows us to examine how genes express themselves in different biological experiments simultaneously. The analysis of gene...
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Gene-CWGAN: a data enhancement method for gene expression profile based on improved CWGAN-GP
Traditional machine learning methods are difficult to obtain good performance in the classification of gene expression data due to its...
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An integrated ensemble learning technique for gene expression classification and biomarker identification from RNA-seq data for pancreatic cancer prognosis
Machine learning (ML) models are used in the interdisciplinary field of bio-ML to solve biological challenges. The diagnosis and treatment of cancer...
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Belayer: Modeling Discrete and Continuous Spatial Variation in Gene Expression from Spatially Resolved Transcriptomics
Motivation: Spatially resolved transcriptomics (SRT) technologies simultaneously measure gene expression and spatial location of cells in a 2D tissue...
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Membrane computing with harmony search algorithm for gene selection from expression and methylation data
Selecting disease-causing genes from gene expression and methylation data with hundreds of thousands of loci is of great benefit for cancer diagnosis...
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SGAClust: Semi-supervised Graph Attraction Clustering of gene expression data
Gene expression data clustering groups genes with similar patterns into a group, while genes exhibit dissimilar patterns into different groups....
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A prediction model for plastic hinge length of rectangular RC columns using gene expression programming
In the reinforced concrete (RC) columns which are exposed extreme loads such as earthquake effects, the plastic hinge length can be defined as the...
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Inference of Gene Regulatory Network (GRN) from Gene Expression Data Using K-Means Clustering and Entropy Based Selection of Interactions
Inferring regulatory networks from gene expression data alone is considered a challenging task in systems biology. The introduction of various...
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An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images
Tumor development is clinically characterized through the manual review of histopathological Whole Slide Images (WSI). However, the molecular...
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Improved swarm-optimization-based filter-wrapper gene selection from microarray data for gene expression tumor classification
A typical microarray dataset usually contains thousands of genes, but only a small number of samples. It is in fact that most genes in a DNA...
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Managing Gene Expression in Evolutionary Algorithms with Gene Regulatory Networks
This paper evaluates the effectiveness of using gene regulatory networks to manage gene expression in evolutionary algorithms for the purpose of...