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Project

Hunting for patient subtypes through image-based phenotypes as biomarkers for major gene effects in medical disorders

To develop a framework for gene-centric image data integration analytics and support a patient stratification strategy that identifies major gene effects with potential applications in a variety of medical disorders. This expands our previous work on rare monogenetic/ complex diseases in craniofacial and neurodevelopmental disorders, using available extensive datasets of different imaging modalities on individuals and patient groups. Mathematics, statistical genetics and deep learning in image analysis (potentially non-linear data-dependencies) will enable data-driven phenotyping from images for patient diagnostics, and stratified screening/subtyping.

Date:20 Aug 2020 →  Today
Keywords:Facial shape image analysis, Statistical genomics, Deep learning, Multi-view learning, Clustering
Disciplines:Bioinformatics data integration and network biology, Computational biomodelling and machine learning
Project type:PhD project