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Related Experiment Video

Updated: Jul 5, 2026

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Published on: August 25, 2023

Network-guided genetic screening: building, testing and using gene networks to predict gene function.

Ben Lehner¹, Insuk Lee

¹EMBL-CRG Systems Biology Research Unit and Institució Catalana de Recerca i Estudis Avançats (ICREA), Centre for Genomic Regulation, UPF, Barcelona 08003, Spain. ben.lehner@crg.es

Briefings in Functional Genomics & Proteomics

|May 1, 2008

Summary

Identifying essential genes for biological processes and diseases is challenging. Integrated gene networks combine diverse data for more accurate gene function prediction, aiding researchers in model organisms and human disease studies.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Identifying genes crucial for biological processes or diseases is a significant challenge for biologists and clinicians.
Experimental data for gene importance prediction is often fragmented across various sources with inconsistent quality.

Purpose of the Study:

To introduce integrated functional gene networks as a method to consolidate dispersed biological data.
To demonstrate how these networks enhance the prediction of gene functions and associations.

Main Methods:

Developing an approach to measure the predictive ability of individual datasets using a standard benchmark.
Combining scored predictions from multiple datasets to create a unified probabilistic gene network.

Main Results:

Integrated gene networks offer greater coverage and accuracy in predicting gene function compared to individual datasets.
The review highlights the construction and validation of these networks, using Caenorhabditis elegans as a model system.

Conclusions:

Integrated gene networks provide a powerful tool for experimental biologists to guide research and predict gene function.
This approach is broadly applicable and holds promise for dissecting the genetics of complex human diseases.