MOCSC: a multi-omics data based framework for cancer subtype classification

Using multi-omics data to achieve the classification of cancer subtypes from the perspective of machine learning can facilitate the understanding of cancer mechanism from the biomolecular aspect, which is of guidance to achieve personalized precision medicine in clinical medicine. To address the pro...

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Vydané v:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) s. 2853 - 2859
Hlavní autori: Ma, Yuanling, Guan, Jinting
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 06.12.2022
Predmet:
classification
Correlation
data integration
Feature extraction
Machine learning
multi-omics data
Neural networks
Noise reduction
Precision medicine
stacked sparse denoising autoencoder
Training
view correlation discovery network
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Shrnutí:Using multi-omics data to achieve the classification of cancer subtypes from the perspective of machine learning can facilitate the understanding of cancer mechanism from the biomolecular aspect, which is of guidance to achieve personalized precision medicine in clinical medicine. To address the problem of cancer subtype classification based on multi-omics data, we propose a framework named MOCSC. Considering the high-dimensional complexity of multi-omics data and the heterogeneity among them, we use the idea of late integration model framework. Specifically, after dividing the training and test sets, for each omics data, the training set is first learned using a stacked sparse denoising autoencoder to extract its feature, and then the extracted feature is supplied to a single-layer neural network to obtain an initial prediction. Then, the initial results of all omics-specific classifiers are integrated and put into a view correlation discovery network for training to obtain the final prediction. Finally, the entire trained model is applied to the test set. The designed model is used to explore the best combination of multi-omics data and then tested on several different datasets. We compare our framework with the existing classification tools, showing its effectiveness in cancer subtype classification problem and also other classification problems based on multi-omics data.
DOI:10.1109/BIBM55620.2022.9995564