Abstract
The use of machine learning approaches in studying cancer through omics datasets has been an important research tool since the advent of high-throughput technologies. However, these datasets present an intrinsic data complexity that may hinder model development despite their information richness. This work, therefore, aims to study the characteristics of different omics data commonly employed for clinical predictive analysis using a broad set of data complexity measures tailored for imbalanced domains. We focus on the task of cancer survival prediction in eight tumor types based on four types of omics data (i.e., copy number variation, gene expression, microRNA expression, and DNA methylation) and the combination among them (i.e., multi-omics approach). We found that F1-MaxDr, F3_partial, F4_partial, and N3_partial could be used as predictors of performance in this scenario. Furthermore, our experiments suggested that the studied omics data types are strongly correlated in terms of data complexity, including the multi-omics approach. All eight cancer types appeared to be highly correlated with each other, except for Adrenocortical Carcinoma (ACC), which showed a significantly lower complexity than the others in the analyzed data.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and by grants from the Fundação de Amparo á Pesquisa do Estado do Rio Grande do Sul (FAPERGS) [21/2551-0002052-0] and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [308075/2021-8].
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The raw results of our experiments can be found in the project Github repository: https://github.com/carlosdanielandrade/complexity-of-omics-data-in-cancer.
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Andrade, C.D., Fontanari, T., Recamonde-Mendoza, M. (2022). Study on the Complexity of Omics Data: An Analysis for Cancer Survival Prediction. In: Scherer, N.M., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2022. Lecture Notes in Computer Science(), vol 13523. Springer, Cham. https://doi.org/10.1007/978-3-031-21175-1_6
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