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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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Related Experiment Video

Updated: Oct 19, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

MIDGET:Detecting differential gene expression on microarray data.

Radu Angelescu¹, Radu Dobrescu¹

¹Department of Automatic Control and Industrial Informatics, Faculty of Automatic Control and Computer Science, University "Politehnica" of Bucharest, Splaiul Independentei nr. 313, Sector 6, Bucuresti, 060042, Romania.

Computer Methods and Programs in Biomedicine

|September 23, 2021

Summary

This summary is machine-generated.

Gene-Bench is a new Python package for evaluating gene expression detection algorithms. It introduces MIDGET, a machine learning tool that improves accuracy in identifying differentially expressed genes.

Keywords:

Deep neural networks Differentially expressed genes Gradient boost Metrics evaluation

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Last Updated: Oct 19, 2025

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Published on: September 18, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Detecting differentially expressed genes is crucial for genome-wide analysis and expression profiling.
Current statistical algorithms may miss-predict gene expression, and a framework for evaluating these algorithms is lacking.
Machine learning methods have not been extensively applied to this problem.

Purpose of the Study:

To introduce Gene-Bench, an open-source Python package for evaluating gene detection algorithms on microarray data.
To provide MIDGET (Machine learning Identification Differential Gene Expression Tool), a novel set of machine learning algorithms for differential gene expression detection.
To offer a comprehensive system for data handling, algorithm evaluation, and benchmarking.

Main Methods:

Gene-Bench incorporates real experimental data (73 transcription-factor and 129 drug perturbation experiments) and synthetic data.
It includes three evaluation metrics: Kolmogorov, F1, and AUC/ROC.
The package implements well-established algorithms alongside new machine learning approaches, including extreme gradient boosting and deep neural networks within MIDGET.

Main Results:

Gene-Bench facilitates flexible prototyping and evaluation of gene detection algorithms.
The MIDGET algorithms demonstrate superior performance across all tested evaluation metrics compared to existing alternatives.
The package is algorithm-independent and supports integration with R language algorithms.

Conclusions:

Gene-Bench addresses the need for a robust framework for evaluating and benchmarking differential gene detection algorithms.
The integrated MIDGET machine learning methods offer higher accuracy in differential gene detection.
Gene-Bench is publicly available on GitHub and installable via pip.