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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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A Hybrid Sequential Feature Selection Approach for Identifying New Potential mRNA Biomarkers for Usher Syndrome Using

Rama Krishna Thelagathoti¹, Wesley A Tom¹, Dinesh S Chandel¹

¹Molecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68131, USA.

|July 29, 2025

Summary

This summary is machine-generated.

This study identifies 58 key mRNA biomarkers for Usher syndrome using machine learning. These biomarkers aid in early detection of the genetic disorder causing hearing and vision loss.

Keywords:

Usher syndrome biomarker detection biomarker validation feature selection genetic disorder detection hybrid feature selection mRNA machine learning transcriptomics

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Area of Science:

Genetics
Bioinformatics
Molecular Biology

Background:

Usher syndrome is a rare genetic disorder causing hearing and vision loss.
Early diagnosis and intervention are critical for managing Usher syndrome.
Identifying reliable biomarkers is essential for effective patient management.

Purpose of the Study:

To develop a machine learning approach for identifying mRNA biomarkers for Usher syndrome.
To reduce the dimensionality of transcriptomic data for biomarker discovery.
To validate the identified biomarkers for diagnostic potential.

Main Methods:

Applied a hybrid sequential feature selection approach using machine learning.
Utilized variance thresholding, recursive feature elimination, and Lasso regression.
Validated biomarkers with Logistic Regression, Random Forest, Support Vector Machines, and ddPCR.

Main Results:

Identified 58 top mRNA biomarkers distinguishing Usher syndrome from controls.
Machine learning models demonstrated robust classification performance.
Experimental validation using ddPCR confirmed biomarker expression patterns.

Conclusions:

Machine learning effectively identifies mRNA biomarkers for Usher syndrome.
The discovered biomarkers show potential for enhancing early detection.
This approach offers a promising strategy for biomarker discovery in genetic disorders.