Dataset Information

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels.

ABSTRACT: In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.

SUBMITTER: Kamisoglu K

PROVIDER: S-EPMC5327699 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Understanding Physiology in the Continuum: Integration of Information from Multiple -<i>Omics</i> Levels.

Kamisoglu Kubra K Acevedo Alison A Almon Richard R RR Coyle Susette S Corbett Siobhan S Dubois Debra C DC Nguyen Tung T TT Jusko William J WJ Androulakis Ioannis P IP

Frontiers in pharmacology 20170227

In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endoto ...[more]

PMID: 28289389

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Project description:MotivationAn unsolved fundamental problem in biology is to predict phenotypes from a new genotype under environmental perturbations. The emergence of multiple omics data provides new opportunities but imposes great challenges in the predictive modeling of genotype-phenotype associations. Firstly, the high-dimensionality of genomics data and the lack of coherent labeled data often make the existing supervised learning techniques less successful. Secondly, it is challenging to integrate heterogeneous omics data from different resources. Finally, few works have explicitly modeled the information transmission from DNA to phenotype, which involves multiple intermediate molecular types. Higher-level features (e.g. gene expression) usually have stronger discriminative and interpretable power than lower-level features (e.g. somatic mutation).ResultsWe propose a novel Cross-LEvel Information Transmission (CLEIT) network framework to address the above issues. CLEIT aims to represent the asymmetrical multi-level organization of the biological system by integrating multiple incoherent omics data and to improve the prediction power of low-level features. CLEIT first learns the latent representation of the high-level domain then uses it as ground-truth embedding to improve the representation learning of the low-level domain in the form of contrastive loss. Besides, CLEIT can leverage the unlabeled heterogeneous omics data to improve the generalizability of the predictive model. We demonstrate the effectiveness and significant performance boost of CLEIT in predicting anti-cancer drug sensitivity from somatic mutations via the assistance of gene expressions when compared with state-of-the-art methods. CLEIT provides a general framework to model information transmissions and integrate multi-modal data in a multi-level system.Availabilityand implementationThe source code is freely available at https://github.com/XieResearchGroup/CLEIT.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels.

Publications

Understanding Physiology in the Continuum: Integration of Information from Multiple -<i>Omics</i> Levels.

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