Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

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dc.authoridPeeters, Ralf/0000-0002-9973-8181
dc.authoridCollins, Pieter/0000-0002-8896-9603
dc.authoridCavill, Rachel/0000-0002-3796-1687
dc.contributor.authorIsmailoglu, Firat
dc.contributor.authorCavill, Rachel
dc.contributor.authorSmirnov, Evgueni
dc.contributor.authorZhou, Shuang
dc.contributor.authorCollins, Pieter
dc.contributor.authorPeeters, Ralf
dc.date.accessioned2024-10-26T18:05:39Z
dc.date.available2024-10-26T18:05:39Z
dc.date.issued2020
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractIncreasingly, 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.
dc.identifier.doi10.1109/TCBB.2018.2877755
dc.identifier.endpage353
dc.identifier.issn1545-5963
dc.identifier.issn1557-9964
dc.identifier.issue1
dc.identifier.pmid30369448
dc.identifier.scopus2-s2.0-85055722468
dc.identifier.scopusqualityQ2
dc.identifier.startpage347
dc.identifier.urihttps://doi.org/10.1109/TCBB.2018.2877755
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29108
dc.identifier.volume17
dc.identifier.wosWOS:000526280900033
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIEEE Computer Soc
dc.relation.ispartofIeee-Acm Transactions on Computational Biology and Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectProteins
dc.subjectBreast cancer
dc.subjectBioinformatics
dc.subjectCurrent measurement
dc.subjectData integration
dc.subjectImmune system
dc.subjectBreast cancer
dc.subjectclassification
dc.subjectheterogeneous domain adaptation
dc.subjecttransfer learning
dc.subjectdata integration
dc.titleHeterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes
dc.typeArticle

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