Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem

Student, Sebastian; Płuciennik, Alicja; Łakomiec, Krzysztof; Wilk, Agata; Bensz, Wojciech; Fujarewicz, Krzysztof

doi:10.1007/978-3-030-24308-1_48

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“…Data fusion could be considered a stage of data integration [ 9 ], and techniques may be classified according to (1) the relationships between the fused source data, such as complementary data or redundant data [ 10 ], (2) the sources of input data [ 11 ], and (3) the level of data processing, such as raw data, preprocessed data, or decisions [ 12 ]. For the purpose of this paper, we define data fusion as a process of merging two feature sets, regardless if the data are of the same size and comparable/of similar origin (such as merging two sets of microarrays) or not (data from heterogenous sources) [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Data Integration–Possibilities of Molecular and Clinical Data Fusion on the Example of Thyroid Cancer Diagnostics

Płuciennik

Płaczek

Wilk

et al. 2022

IJMS

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(1) Background: The data from independent gene expression sources may be integrated for the purpose of molecular diagnostics of cancer. So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. (2) Methods: We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. (3) Results: We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a p-value below 0.05) and with a reduction in molecular dimensionality of the feature space from 15 to 3–8 (depending on the feature selection method). (4) Conclusions: Both strategies give comparable quality results, but the early fusion method provides better feature selection stability.

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