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    Heterogeneous Domain Adaptation Based on Class Decomposition Schemes
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2018) Ismailoglu, Firat; Smirnov, Evgueni; Peeters, Ralf; Zhou, Shuang; Collins, Pieter; Phung, D; Tseng, VS; Webb, GI; Ho, B; Ganji, M; Rashidi, L
    This paper introduces a novel classification algorithm for heterogeneous domain adaptation. The algorithm projects both the target and source data into a common feature space of the class decomposition scheme used. The distinctive features of the algorithm are: (1) it does not impose any assumptions on the data other than sharing the same class labels; (2) it allows adaptation of multiple source domains at once; and (3) it can help improving the topology of the projected data for class separability. The algorithm provides two built-in classification rules and allows applying any other classification model.
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    Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes
    (IEEE Computer Soc, 2020) Ismailoglu, Firat; Cavill, Rachel; Smirnov, Evgueni; Zhou, Shuang; Collins, Pieter; Peeters, Ralf
    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 percent 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.