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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Multi-species Conserved Sequences

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Genomics02:02

Genomics

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Genome-wide Association Studies-GWAS01:11

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines.

Sanvesh Srivastava1, Wenyi Wang, Ganiraju Manyam

  • 1Department of Biostatistics, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1411, Houston, Texas, USA. veera@mdanderson.org.

EURASIP Journal on Bioinformatics & Systems Biology
|July 2, 2013
PubMed
Summary
This summary is machine-generated.

We developed a machine learning model to integrate genomic data for predicting cancer outcomes. This approach improves prediction accuracy by modeling complex interactions, aiding personalized cancer treatment.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Genomic technologies generate high-throughput data, necessitating advanced methods for target discovery.
  • Integrating diverse genomic data for clinical prediction is challenging due to high dimensionality and complex interactions.
  • Existing methods often fail to capture intricate relationships within and across genomic platforms.

Purpose of the Study:

  • To develop a flexible statistical machine learning approach for clinical prediction using multi-platform genomic data.
  • To model interactions between platform-specific measurements using nonlinear kernel machines.
  • To integrate information across platforms via a hierarchical Bayesian framework.

Main Methods:

  • A hierarchical Bayesian model incorporating nonlinear kernel machines to capture interactions.
  • A variational Bayes approach for computationally efficient parameter estimation in large datasets.
  • Application to The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) dataset, integrating gene/mRNA and microRNA expression profiles.

Main Results:

  • The integrative, nonlinear, and interaction-aware model significantly outperformed linear and non-integrative methods in prediction accuracy.
  • Identified prognostic mRNAs and microRNAs associated with GBM tumor invasion, metastasis, and inflammation.
  • Revealed significant mRNA-microRNA interactions implicated in the etiology of glioblastoma.

Conclusions:

  • The proposed framework effectively models complex, nonlinear interactions within and between genomic platforms.
  • This approach enhances clinical prediction using multi-platform genomic data, supporting personalized cancer therapy.
  • Freely available software facilitates its use by biomedical researchers.