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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Unlocking proteomic heterogeneity in complex diseases through visual analytics.

Suresh K Bhavnani¹, Bryant Dang, Gowtham Bellala

¹Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA; Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, TX, USA; Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, TX, USA.

|February 17, 2015

Summary

This summary is machine-generated.

Visual analytics and network methods can reveal patient heterogeneity missed by traditional analyses, improving drug development for complex diseases like asthma. This approach aids in designing biomarker-based trials and personalizing medicine.

Keywords:

Bioinformatics Molecular and clinical profiles Network analysis Personalized medicine Proteomic heterogeneity Subject-Protein Networks

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Area of Science:

Biomedical Data Analysis
Translational Medicine
Visual Analytics

Background:

Complex diseases like asthma often see biological interventions fail in late-stage clinical trials.
Current biomedical data analysis may overlook patient heterogeneity by assuming homogeneous distributions in cases and controls.

Purpose of the Study:

To explore how visual analytics methods can address the challenge of modeling and inferring heterogeneity in patient proteomic and phenotypic profiles.
To demonstrate the utility of subject-protein networks for analyzing molecular heterogeneities in complex diseases.

Main Methods:

Overview of the cognitive foundations of visual analytics.
Discussion of network-based visual analytics for modeling biological mechanisms.
Application of subject-protein networks on two proteomic datasets.

Main Results:

Subject-protein networks can identify properties useful for discovering and analyzing proteomic heterogeneity.
Demonstration provides insights into overcoming hurdles for using subject-protein networks.

Conclusions:

Visual analytics, particularly subject-protein networks, can help translational teams analyze molecular heterogeneities.
This approach can accelerate the design of biomarker-based clinical trials and personalized medicine.