Detail publikace

Combining Gene Expression and Clinical Data to Increase Performance of Prognostic Breast Cancer Models

ŠILHAVÁ, J. SMRŽ, P.

Originální název

Combining Gene Expression and Clinical Data to Increase Performance of Prognostic Breast Cancer Models

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Microarray class prediction is an important application of gene expression data in biomedical research. Combining gene expression data with other relevant data may add valuable information and can generate more accurate prognostic predictions. In this paper, we combine gene expression data with clinical data. We use logistic regression models that can be built through various regularized techniques. Generalized linear models enables combining of these models with different structure of data. Our two suggested approaches are evaluated with publicly available breast cancer data sets. Based on the results, our approaches have a positive effect on prediction performances and are not computationally intensive.

Klíčová slova

generalized linear models, logistic regression, regularization, combined data, gene expression data

Autoři

ŠILHAVÁ, J.; SMRŽ, P.

Rok RIV

2012

Vydáno

1. 2. 2012

Nakladatel

Institute for Systems and Technologies of Information, Control and Communication

Místo

Algarve

ISBN

978-989-8425-95-9

Kniha

4th International Conference on Agents and Artificial Intelligence

Strany od

1

Strany do

6

Strany počet

6

BibTex

@inproceedings{BUT97081,
  author="Jana {Šilhavá} and Pavel {Smrž}",
  title="Combining Gene Expression and Clinical Data to Increase Performance of Prognostic Breast Cancer Models",
  booktitle="4th International Conference on Agents and Artificial Intelligence",
  year="2012",
  pages="1--6",
  publisher="Institute for Systems and Technologies of Information, Control and Communication",
  address="Algarve",
  isbn="978-989-8425-95-9"
}