2026-04-29T12:23:08Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/78292024-11-29T13:38:00Zcom_20.500.12105_2173com_20.500.12105_2051col_20.500.12105_19597

00925njm 22002777a 4500 dc Lopez de Maturana, Evangelina author Alonso, Lola author Alarcón, Pablo author Martín-Antoniano, Isabel Adoración author Pineda, Silvia author Piorno, Lucas author Calle, M Luz author Malats, Nuria author 2019-03-20 Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm's predictive ability. Only a small number of published studies performed a "real" integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration. Genes (Basel). 2019;10(3). pii: E238. 10.3390/genes10030238 2073-4425 Genes 30897838 http://hdl.handle.net/20.500.12105/7829 RNA expression Challenges Clinical data Data integration Epidemiological data Genomics Integrative analytics Joint modeling Non-omics data Omics data Challenges in the Integration of Omics and Non-Omics Data