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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Oct 14, 2025

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Published on: March 30, 2019

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.

Vijit Saini^1,2, Mugdha V Joglekar¹, Wilson K M Wong¹

¹Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Narellan Road & Gilchrist Drive, Campbelltown, NSW 2560, Australia.

|November 8, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning workflow to identify key microRNAs (miRNAs) and optimized methods for their validation in biological research. These protocols aid in understanding gene regulation in disease.

Keywords:

Bioinformatics Cell culture Computer sciences Gene Expression Molecular Biology

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Last Updated: Oct 14, 2025

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

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Published on: March 30, 2019

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Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes

Differentiation of a Human Neural Stem Cell Line on Three Dimensional Cultures, Analysis of MicroRNA and Putative Target Genes

Published on: April 12, 2015

Area of Science:

Molecular Biology
Bioinformatics
Genetics

Background:

MicroRNAs (miRNAs) are crucial regulators within gene networks.
Understanding miRNA roles in disease requires precise manipulation and identification.
High-dimensional biological data necessitates advanced analytical approaches.

Purpose of the Study:

To present a comprehensive workflow for identifying significant miRNAs using machine learning.
To detail optimized experimental techniques for validating identified miRNAs.
To provide adaptable protocols for diverse biological research fields.

Main Methods:

Application of a machine learning algorithm for miRNA identification from high-dimensional data.
Implementation of over-expression and loss-of-function studies for miRNA validation.
Development of a detailed workflow applicable to various biological datasets.

Main Results:

Successful identification of key microRNAs implicated in patho-physiological conditions.
Established optimized protocols for experimental validation of miRNA functions.
Demonstrated the utility of the workflow across different biological research areas.

Conclusions:

The presented workflow effectively identifies and validates crucial miRNAs.
These protocols enhance the study of gene regulatory networks in biological and pathological contexts.
The methodology is broadly applicable to high-dimensional biological data analysis.