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Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Biological cluster evaluation for gene function prediction.

Sebastian Klie¹, Zoran Nikoloski, Joachim Selbig

¹1 Max-Planck Institute for Molecular Plant Physiology , Potsdam, Brandenburg, Germany .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|January 12, 2010

Summary

This summary is machine-generated.

This study introduces a new framework to analyze gene expression data, overcoming limitations in determining gene cluster numbers and evaluating biological relevance. It enables accurate gene function prediction using structured biological knowledge.

Keywords:

NP-completeness algorithms biochemical networks combinatorics computational molecular biology databases functional genomics gene expression

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

High-throughput omics enable gene function discovery via guilt-by-association on gene expression clusters.
Existing frameworks face bottlenecks in selecting the optimal number of clusters and utilizing structured biological knowledge for evaluation.

Purpose of the Study:

To develop a novel framework addressing bottlenecks in gene cluster analysis and biological evaluation.
To enable accurate prediction of putative gene functions based on structured biological knowledge and data structure.
To improve the biological evaluation of gene expression clusters.

Main Methods:

Developed a framework for biological evaluation and gene function prediction of gene expression clusters.
Incorporated novel external structural measures and ontology-based evaluation for gene clusters.
Utilized a probabilistic method for estimating the number of clusters and network-based approach for function prediction.

Main Results:

The framework successfully estimates the number of clusters and evaluates them using novel structural measures.
Demonstrated validity on synthetic data and gene expression profiles of Saccharomyces cerevisiae.
Effectively predicted gene functions for Saccharomyces cerevisiae and Arabidopsis thaliana datasets.

Conclusions:

The novel framework overcomes limitations in current gene cluster analysis, enabling robust biological evaluation.
The proposed method facilitates accurate prediction of gene functions by leveraging structured biological knowledge and network analysis.
This approach enhances the utility of high-throughput omics data for understanding gene function.