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Genomics, transcriptomics, proteomics and big data analysis in the discovery of new diagnostic markers and targets for therapy development

Research output: Contribution to book/anthology/report/proceedingBook chapterCommunication

Highly complex endophenotypes and underlying molecular mechanisms have prevented effective diagnosis and treatment of autism spectrum disorder. Despite extensive studies to identify relevant biosignatures, no biomarker and therapeutic targets are available in the current clinical practice. While our current knowledge is still largely incomplete, -omics technology and machine learning-based big data analysis have provided novel insights on the etiology of autism spectrum disorders, elucidating systemic impairments that can be translated into biomarker and therapy target candidates. However, more integrated and sophisticated approaches are vital to realize molecular stratification and individualized treatment strategy. Ultimately, systemic approaches based on -omics and big data analysis will significantly contribute to more effective biomarker and therapy development for autism spectrum disorder.

Original languageEnglish
Title of host publicationAutism
EditorsMirolyuba Ilieva, Way Kwok-Wai Lau
Number of pages30
PublisherElsevier
Publication year2020
Pages61-90
Chapter3
ISBN (print)9780128212424
DOIs
Publication statusPublished - 2020
SeriesProgress in Molecular Biology and Translational Science
Volume173
ISSN1877-1173

    Research areas

  • Autism spectrum disorder, Big data analysis, Genomics, Machine learning, Proteomics, Transcriptomics

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ID: 202059597