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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Published on: March 1, 2024

Social network Analysis-based classifier (SNAc): A case study on time course gene expression data.

Serkan Üçer¹, Yunuscan Koçak¹, Tansel Ozyer¹

¹Department of Computer Engineering, TOBB University, Ankara, Turkey; Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.

Computer Methods and Programs in Biomedicine

|September 2, 2017

Summary

This summary is machine-generated.

This study introduces a novel Social Network Analysis-based Classifier for time sequential genomic data. The model effectively classifies genomic datasets, outperforming traditional methods with 64.51% accuracy.

Keywords:

Gene expression data Genomic interactions Social network analysis Social network classifier

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Area of Science:

Bioinformatics
Computational Biology
Network Science

Background:

Social Network Analysis (SNA) is increasingly applied to complex biological networks.
Genomic data presents unique challenges for traditional classification methods due to its time-sequential nature.

Purpose of the Study:

To develop a novel classifier for time sequential data using Social Network Analysis.
To model genomic data as a 'social' network of patient interactions.

Main Methods:

Genomic time sequential data was represented as a 'genomic social network' based on patient measurement similarity.
A classification model, the Social Network Analysis-based Classifier, was developed using this network representation.

Main Results:

The Social Network Analysis-based Classifier demonstrated superior performance in classifying a time sequential genomic dataset.
The best achieved classification accuracy was 64.51%, with a best f-measure of 78.34%.

Conclusions:

The Social Network Analysis-based Classifier is a powerful technique for analyzing time sequential datasets, particularly in genomics.
Future work aims to evolve this model into a general-purpose classifier applicable to diverse data types.