Publication detail

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

ŠILHAVÁ, J. SMRŽ, P.

Original Title

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

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

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.

Keywords

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

Authors

ŠILHAVÁ, J.; SMRŽ, P.

RIV year

2012

Released

1. 2. 2012

Publisher

Institute for Systems and Technologies of Information, Control and Communication

Location

Algarve

ISBN

978-989-8425-95-9

Book

4th International Conference on Agents and Artificial Intelligence

Pages from

1

Pages to

6

Pages count

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"
}