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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Support Vector Machines with Disease-gene-centric Network Penalty for High Dimensional Microarray Data.

Yanni Zhu¹, Wei Pan, Xiaotong Shen

¹Division of Biostatistics, School of Public Health, University of Minnesota.

Statistics and Its Interface

|April 20, 2010

Summary

This summary is machine-generated.

We developed a new Disease-Gene-Centric Support Vector Machine (DGC-SVM) to improve disease gene detection using genetic networks. This method enhances gene selection for microarray classification by focusing on disease-related genes.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Bioinformatics
Computational Biology
Machine Learning in Genomics

Background:

Leveraging genetic pathways and known disease genes is crucial for understanding complex diseases.
Existing machine learning methods for microarray analysis may not fully utilize prior biological network information.
Accurate identification of disease-associated genes is essential for developing effective diagnostic and prognostic tools.

Purpose of the Study:

To propose a novel Disease-Gene-Centric Support Vector Machine (DGC-SVM) for binary classification using microarray data.
To integrate prior knowledge of genetic networks and disease genes directly into the classification model.
To enhance the detection and selection of relevant disease genes, particularly those weakly associated with outcomes.

Main Methods:

Developed DGC-SVM, a classifier that incorporates gene network topology and known disease gene information.
Introduced a penalty function based on the L(infinity)-norm applied to hierarchically defined gene groups within the network.
Evaluated performance through simulation studies and real-world microarray data applications, comparing against standard SVM and L1-SVM.

Main Results:

DGC-SVM identified more disease genes located along biological pathways compared to standard SVM and L1-SVM.
The method successfully captured weakly associated disease genes, improving comprehensive gene discovery.
Real data applications showed improved gene selection with classification performance comparable to existing methods.

Conclusions:

DGC-SVM effectively integrates genetic network and disease gene prior information for improved microarray classification.
The proposed method offers a powerful tool for gene selection, prioritizing genes within or around known disease gene networks.
DGC-SVM has the potential to advance the analysis of genomic data for disease gene discovery and classification.