Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

Firat Ismailoglu, Rachel Cavill, Evgueni Smirnov, Shuang Zhou, Pieter Collins, Ralf Peeters

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)347-353
Number of pages7
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume17
Issue number1
Early online date24 Oct 2018
DOIs
Publication statusPublished - Feb 2020

Keywords

  • Bioinformatics
  • Breast cancer
  • CHALLENGES
  • Current measurement
  • Data integration
  • FEATURES
  • Immune system
  • Proteins
  • SIGNATURES
  • classification
  • data integration
  • heterogeneous domain adaptation
  • transfer learning

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