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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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Biological Network Inference from Microarray Data, Current Solutions, and Assessments.

Swarup Roy¹, Pietro Hiram Guzzi²

¹Department of Information Technology, North-Eastern Hill University, Shillong, India. swarup@nehu.ac.in.

Methods in Molecular Biology (Clifton, N.J.)

|October 29, 2015

Summary

This summary is machine-generated.

Bioinformatics and systems biology increasingly focus on biological network inference from experimental data. This study surveys inference algorithms, compares assessment methods, and lists available tools, highlighting future research directions.

Keywords:

Assessment Biological network inference Gene Ontology Gene regulatory network Gold standard Graph theory

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

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Published on: March 15, 2011

Area of Science:

Bioinformatics and Systems Biology
Computational Biology
Network Science

Background:

Growing interest in analyzing biological molecule associations at a network level.
Biological network inference aims to reconstruct interaction networks from experimental data.
Numerous inference algorithms exist, but fair assessment and comparison methods are underdeveloped.

Purpose of the Study:

To survey selected biological network inference algorithms.
To compare current assessment methodologies for these algorithms.
To provide a resource of freely available inference and assessment tools.

Main Methods:

Survey of existing biological network inference algorithms.
Comparative analysis of current assessment strategies using reference networks or gold standard datasets.
Systematic compilation of freely available software tools for inference and assessment.

Main Results:

Identified a gap in standardized fair assessments and comparisons of network inference algorithms.
Presented a comparative overview of selected inference algorithms and their assessment methods.
Compiled a list of accessible bioinformatics tools for network inference and evaluation.

Conclusions:

There is a need for improved and standardized methods for assessing biological network inference algorithms.
Future research should explore incorporating prior knowledge into the assessment process.
Accessible tools are crucial for advancing the field of biological network inference.