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GeNets: a unified web platform for network-based genomic analyses.

Taibo Li^1,2,3,4, April Kim^1,2, Joseph Rosenbluh^2,5

¹Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.

|June 20, 2018

Summary

This summary is machine-generated.

GeNets allows users to train machine-learning models to compare functional genomics networks. This platform aids in selecting the best network for interpreting genetic data, improving signal-to-noise ratios.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Functional genomics networks are crucial for discovering pathway relationships in large genomic datasets.
Comparing the signal-to-noise ratios of different networks is a significant challenge.
Identifying the optimal network for specific genetic datasets is difficult.

Purpose of the Study:

To present GeNets, a platform for comparing functional genomics networks.
To enable users to train machine-learning models for network comparison.
To facilitate the execution, storage, and sharing of genetic and RNA-sequencing data analyses.

Main Methods:

Development of the GeNets platform.
Implementation of a machine-learning model named Quack for network comparison.
User-guided training of the Quack model.

Main Results:

GeNets provides a framework for users to train machine-learning models.
The platform facilitates the comparison of signal-to-noise ratios across different networks.
Users can execute, store, and share analyses of genetic and RNA-sequencing datasets.

Conclusions:

GeNets offers a novel solution for selecting optimal functional genomics networks.
The platform enhances the interpretation of genetic datasets through improved network comparison.
GeNets supports reproducible and shareable bioinformatics analyses.