Classification of Human Cancer Diseases Gene Expression Profiles using Genetic Algorithm by Integrating Protein Protein Interactions Along with Gene Expression Profiles

Daisy, A.; Porkodi, R

doi:10.1109/icctct.2018.8550878

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E Comparación Con Resultados De La Literatura2

Ppi Network and Diseases1

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Cited by 2 publications

(3 citation statements)

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“…Los autores informan el número de evaluaciones utilizadas como 2500 y 20000 evaluaciones, respectivamente. También, se presentan los resultados de [35]- [37] aunque en estos trabajos no informan explícitamente cuántas iteraciones o evaluaciones han realizado para obtener sus resultados. Los algoritmos utilizados por cada uno de ellos Fig.…”

Section: E Comparación Con Resultados De La Literaturaunclassified

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A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Rojas

Olivera

Carballido

et al. 2020

IEEE Latin Am. Trans.

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Section: E Comparación Con Resultados De La Literaturaunclassified

“…• algoritmo genético con técnica de maximización de la información mutua condicional (AG+MIMC) [34]. • algoritmo genético (AG) [35].…”

Section: E Comparación Con Resultados De La Literaturaunclassified

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Rojas

Olivera

Carballido

et al. 2020

IEEE Latin Am. Trans.

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“…PPI network demonstrates the specificity between proteins. Every PPI comprises of seed protein or candidate protein with direct neighbors [30].…”

Section: Ppi Network and Diseasesmentioning

confidence: 99%

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

Ali¹

2019

IJATCSE

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Proteins interact each other to perform many cellular activities. These interactions can be considered as Protein Protein Interaction networks (PPI). Interacting proteins form protein complexes. Mapping nodes between networks is denoted as alignment. The main intention of network alignment approach is to identify the protein complexes, which in turn helps to identify the functionality of protein complexes in various cellular systems. These interactome units form the conserved pathways between the networks. So network alignment requires lot of attention and several algorithms and techniques have been proposed to address this. The study of PPI is widely recognized to know more about the underlying complex disease because proteins associated with any disease get connected and form subgraphs or pathways. In this paper, the authors compared the various aligners, the performance evaluation metrics, the common databases used for PPI evaluation and the importance of PPI network in biomedical research.

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Classification of Human Cancer Diseases Gene Expression Profiles using Genetic Algorithm by Integrating Protein Protein Interactions Along with Gene Expression Profiles

Cited by 2 publications

References 9 publications

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

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