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A Multi-Task Ensemble Strategy for Gene Selection and Cancer Classification. [PDF]

open access: yesBioengineering (Basel)
Gene expression-based tumor classification aims to distinguish tumor types based on gene expression profiles. This task is difficult due to the high dimensionality of gene expression data and limited sample sizes.
Lin S, Lin Z, Zhang J, Leung MF.
europepmc   +2 more sources

Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification. [PDF]

open access: yesDiagnostics (Basel)
Background: Ovarian cancer is a difficult and lethal illness that requires early detection and precise classification for effective therapy. Microarray technology has permitted the simultaneous assessment of hundreds of genes’ expression levels, yielding
Lee ZJ, Cai JX, Wang LH, Yang MR.
europepmc   +2 more sources

A Hybrid Ensemble Equilibrium Optimizer Gene Selection Algorithm for Microarray Data. [PDF]

open access: yesBiomimetics (Basel)
As modern medical technology advances, the utilization of gene expression data has proliferated across diverse domains, particularly in cancer diagnosis and prognosis monitoring. However, gene expression data is often characterized by high dimensionality
Su P, Zhao Y, Li X, Ma Z, Wang H.
europepmc   +2 more sources

Hybrid Gene Selection Algorithm for Cancer Classification Using Nuclear Reaction Optimization (NRO). [PDF]

open access: yesCurr Issues Mol Biol
Microarray gene expression data are characterized by high dimensionality and small sample sizes, which complicates cancer classification tasks. To address these challenges, this study proposes a hybrid gene selection approach that integrates a filter ...
Alkamli S, Alshamlan H.
europepmc   +2 more sources

Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification. [PDF]

open access: yesDiagnostics (Basel)
Background/Objectives: Cancer classification using microarray datasets presents a significant challenge due to their extremely high dimensionality. This complexity necessitates advanced optimization methods for effective gene selection.
Alkamli S, Alshamlan H.
europepmc   +2 more sources

DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network. [PDF]

open access: yesFront Genet
Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine.
Pak M, Bang D, Sung I, Kim S, Lee S.
europepmc   +2 more sources

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

open access: yesFrontiers in Genetics, 2020
Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription.
Chuan-Yu Chang   +2 more
exaly   +3 more sources

A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study

open access: yesAnimals, 2022
Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important ...
Maoxuan Miao   +3 more
doaj   +1 more source

Enhancing Feature Selection Optimization for COVID-19 Microarray Data

open access: yesCOVID, 2023
The utilization of gene selection techniques is crucial when dealing with extensive datasets containing limited cases and numerous genes, as they enhance the learning processes and improve overall outcomes.
Gayani Krishanthi   +4 more
doaj   +1 more source

Selective gene amplification [PDF]

open access: yesProtein Engineering Design and Selection, 2007
We describe a system for directed evolution based on in vitro compartmentalisation in which amplification of a gene is coupled to the formation of product by the enzyme it encodes. This approach mimics the process of natural selection; 'fitter' genes--encoding more efficient enzymes--have more 'offspring'.
Bernard T, Kelly, Andrew D, Griffiths
openaire   +2 more sources

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