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Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Published on: January 13, 2016

A robust topology-based algorithm for gene expression profiling.

Lars Seemann¹, Jason Shulman², Gemunu H Gunaratne¹

¹Department of Physics, University of Houston, Houston, TX 77204, USA.

ISRN Bioinformatics

|May 14, 2015

Summary

This summary is machine-generated.

This study introduces a novel computational approach using topology for cancer subtyping, improving accuracy and efficiency in classifying glioblastoma multiforme (GBM) tumors. The method identifies key genes and patient groups for precise cancer subtype prediction.

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Last Updated: Apr 12, 2026

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Published on: January 13, 2016

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Area of Science:

Computational Biology
Genomics
Topology

Background:

Accurate cancer diagnosis is crucial for personalized therapy and treatment success.
Traditional histopathology is time-consuming, hindering timely diagnoses.
High-throughput genomics offers new cancer classification, but existing algorithms lack robustness.

Purpose of the Study:

To develop a novel, robust computational method for cancer subtyping using statistical invariants and persistent homology.
To identify compact gene sets and core patient groups for each cancer subtype.
To improve the accuracy and efficiency of cancer subtype classification.

Main Methods:

Application of statistical invariants and persistent homology for data analysis.
Hierarchical partitioning to identify genetically similar cancer subtypes.
Analysis of gene expression profiles from 202 glioblastoma multiforme (GBM) tumors via The Cancer Genome Atlas (TCGA).

Main Results:

Identification of core patient groups associated with classical, mesenchymal, and proneural GBM subtypes.
The neural subtype was found to comprise multiple smaller groups, not a single component.
A subtype prediction model was developed, requiring only 59 genes for tumor partitioning.

Conclusions:

The novel topological approach provides a robust method for cancer subtyping.
The method efficiently identifies key genetic markers and patient cohorts for distinct subtypes.
This approach enhances personalized medicine by enabling more accurate and timely cancer classification.