Ph.D.Principal Investigator in-charge
National Neuroscience Institute11 Jalan Tan Tock Seng, Singapore 308433Tel: (65) 6357 7616 / (65) 6357 7634 (Lab) Fax: (65) 6256 9178 Email: firstname.lastname@example.org
Our newly formed division has its roots from our early industry collaboration in neuro-oncology. This collaboration focused on establishing patient stratification methods, using patient-derived xenografts and primary glioma cells. To overcome the low statistical power of primary cell lines, as with any such studies, we tapped into the large public clinical databases to form informative associations between gene activation pathways, somatic mutation profiles, clinical prognosis and imaging sequences. We established computational platforms to facilitate these associative studies (Choudhury et al, J Clin Invest, 2012; Koh et al, Antioxid Redox Signal, 2013; Ng et al, Clin Cancer Res, 2012Yeo et al, Cancer Res, 2012) .
In advancing genomic interrogative studies for the various neurological diseases at NNI, we envisage expanding our neuro-computing platform to assess the clinical phenotype-genotype relationship. NNI provides medical treatment services for the majority of local and regional patients with central nervous system disorders. As such, we have a substantial pool of patients and their associated clinical material for research endeavors. Our detailed longitudinal history of, for example, Parkinson’s disease patients, with recently acquired exome-profiling data, allows us to identify potential biomarkers and process networks associated with disease etiology. In addition, our efforts in “Precision Medicine” allow us to identify patient cohorts most likely to benefit from targeted treatment approaches.
Deployment of Informatics Strategies for Characterizing Clinical Neurological Disorders
Statistical models will be adapted for studies such as genomic, gene expression, methylation, Next-generation sequencing and microRNA analyses. These interrogation methods are common across all diseases in the various themes with focus on patient stratification and prognostic marker identification.
Synergy and Cohesion Between Disease Themes
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