Identifying network-based biomarkers of complex diseases from high-throughput data

Biomarkers in Medicine
Zhi-Ping Liu

Abstract

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

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Citations

Jun 10, 2016·Biomarkers in Medicine·Hao-Teng Chang
Mar 15, 2019·Journal of Bioinformatics and Computational Biology·Anna V KudryavtsevaGeorge S Krasnov
Jul 27, 2018·BMC Systems Biology·Rajat AnandSamrat Chatterjee
Dec 10, 2020·Computational and Structural Biotechnology Journal·Lingyu Li, Zhi-Ping Liu
Aug 27, 2021·BMC Medical Genomics·Xue JiangGuan Ning Lin

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Methods Mentioned

BETA
dissections

Software Mentioned

DNB
Reactome
Bioconductor
edgeR
R
PRoteomics IDEntification ( PRIDE )
ImmGene
ArrayExpress
DESeq
DiseaseConnect

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