<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization.</p>
</abstract>

Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization.

Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

University of Eastern Piedmont Amedeo Avogadro

Communications in Applied and Industrial Mathematics

This work is licensed under the Creative Commons Attribution 4.0 International License.

Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease...

Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

Data publikacji: 10 sie 2021

Zakres stron: 29 - 47

Otrzymano: 02 lut 2021

Przyjęty: 06 lip 2021

DOI: https://doi.org/10.2478/caim-2021-0003

Słowa kluczowe
genetic expression, bladder cancer, discretization, survival rate modeling, data-driven biomarker research, machine learning

© 2021 Mauro Nascimben et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Dodatkowe informacje o materiałach

Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

Data publikacji: 10 sie 2021

Zakres stron: 29 - 47

Otrzymano: 02 lut 2021

Przyjęty: 06 lip 2021

DOI: https://doi.org/10.2478/caim-2021-0003

Słowa kluczowegenetic expression, bladder cancer, discretization, survival rate modeling, data-driven biomarker research, machine learning

© 2021 Mauro Nascimben et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Dodatkowe informacje o materiałach

Słowa kluczowe
genetic expression, bladder cancer, discretization, survival rate modeling, data-driven biomarker research, machine learning