Naslov
Contextual analysis and visualization of high- throughput data

Autori
Glažar, Petar

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad

Fakultet
Prirodoslovno-matematički fakultet

Mjesto
Zagreb

Datum
07.12

Godina
2011

Stranica
34

Mentor
Vlahoviček, Kristian

Ključne riječi
microarray; metabolic networks; metabolic pathways; signal transduction networks; transcription networks; enrichment analysis

Sažetak
We are witnessing an increase of data available from high-throughput –omics experiments, which present a great potential for novel biological discoveries. In order to efficiently extract knowledge from the collected information, computational solutions for data analysis and visualization are needed. MADNet is a platform aimed at broadening the context by integrating high-throughput experimental data with diverse biological information. MADNet is subject to continuous improvements and feature upgrades, and I have expanded it with new features and rebuilt it to implement new technologies. Metagenomic data may now be analyzed for enrichment of KEGG metabolic pathways, Gene Ontology functional annotation is supported as an enrichment context, and protein interactions of submitted data may be explored. With additional back-end upgrades, improved interface and information-dense visualizations, MADNet gives users the opportunity to extract hidden biological knowledge by exploring the data in the context of biological systems – metabolic pathways, gene networks and regulation cascades.

Izvorni jezik
Engleski

Znanstvena područja
Biologija

POVEZANOST RADA