ArticleBMC bioinformatics2014
iGPSe: a visual analytic system for integrative genomic based cancer patient stratification.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Exploring and visualizing multidimensional data in translational research platforms.Briefings in bioinformatics · 2017Article
- Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis.Cancer research · 2017Article
- Interactive visual exploration and refinement of cluster assignments.BMC bioinformatics · 2017Article
- Article
- Health Informatics via Machine Learning for the Clinical Management of Patients.Yearbook of medical informatics · 2015Review
- PICan: An integromics framework for dynamic cancer biomarker discovery.Molecular oncology · 2015Article
- XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data.BMC bioinformatics · 2015Article
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Authors and funding
4 authors.
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Abstract
backgroundCancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes.
resultsIn this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction.
conclusionsVisualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.