2026-04-29T12:22:47Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/78292024-11-29T13:38:00Zcom_20.500.12105_2173com_20.500.12105_2051col_20.500.12105_19597
Repisalud
author
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
funder
Unión Europea. European Cooperation in Science and Technology (COST)
funder
World Cancer Research Fund International
funder
Ministerio de Economía y Competitividad (España)
funder
Instituto de Salud Carlos III
2019-07-02T05:39:44Z
2019-07-02T05:39:44Z
2019-03-20
Genes (Basel). 2019;10(3). pii: E238.
10.3390/genes10030238
2073-4425
Genes
30897838
http://hdl.handle.net/20.500.12105/7829
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.
eng
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
journal article
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ChallengesintheIntegrationofOmicsandNon-Omics_2019.pdf
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ChallengesintheIntegrationofOmicsandNon-Omics_2019.pdf.txt