Research output

Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

Research output: Contribution to journalArticleAcademicpeer-review

Standard

Harvard

APA

Vancouver

Author

Bibtex

@article{7af20bc05c77499c9ce5d91a3814f862,
title = "Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes",
abstract = "Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g. just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12{\%} in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.",
author = "Firat Ismailoglu and Rachel Cavill and Evgueni Smirnov and Shuang Zhou and Pieter Collins and Ralf Peeters",
year = "2018",
month = "10",
day = "24",
doi = "10.1109/TCBB.2018.2877755",
language = "English",
journal = "Ieee-Acm Transactions on Computational Biology and Bioinformatics",
issn = "1545-5963",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

AU - Ismailoglu, Firat

AU - Cavill, Rachel

AU - Smirnov, Evgueni

AU - Zhou, Shuang

AU - Collins, Pieter

AU - Peeters, Ralf

PY - 2018/10/24

Y1 - 2018/10/24

N2 - Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g. just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12% in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

AB - Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g. just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12% in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

U2 - 10.1109/TCBB.2018.2877755

DO - 10.1109/TCBB.2018.2877755

M3 - Article

JO - Ieee-Acm Transactions on Computational Biology and Bioinformatics

T2 - Ieee-Acm Transactions on Computational Biology and Bioinformatics

JF - Ieee-Acm Transactions on Computational Biology and Bioinformatics

SN - 1545-5963

ER -