The biobanking facility maintains a repository of clinical samples collected from cohort studies, and establishes standard protocols for the collection, archiving, and distribution of clinical biosamples. Our biobanking specialists also advise on sample resourcing for research proposal and provide consultancy for derivation of nucleic acids in standardised formats.
The data management facility oversees management cleaning, and data archiving, as well as statistical support. It safeguards the data collected from our clinical studies, ensuring that research analyses can draw on high-quality, reliable data.
The Core Molecular Laboratory operates within the framework of SICS and aims to streamline wet laboratory procedures to ensure efficient processing and analyses of clinical samples. The main focus of the Core Molecular Laboratory is to facilitate clinical research by providing specialised expertise and protocols in tissue and cell culture, biochemistry and molecular biology. The resulting molecular data complements both clinical research and systems biology, providing capacity in both the ‘bench-to-bedside’ and ‘bedside-to-bench’ paradigms. Although the laboratory works predominantly on nucleic acids, proteins and more recently lipids, we strive to update on innovations in laboratory techniques and protocols. In addition to human samples, the laboratory has experience with clinically relevant animal models (non-human primates and rodent models). The Molecular programme benefits from multiple partnerships, including the Department of Biochemistry at NUS and the Genome Institute of Singapore.
Data is only as valuable as the decisions it enables, which is why the Biostatistics programme is a cornerstone of the work we do at SICS. Through the application of cutting-edge epidemiologic and biostatistical methods to complex data acquired from clinical and population health projects, we are able to develop a deeper understanding of maternal, child, and adolescent health. Some of these methods include longitudinal causal inference approaches to investigating developmental mechanisms and estimating potential benefits of early life interventions, and principled consideration of machine learning algorithms for developing and testing prediction models for maternal and child outcomes. Notable projects include simulating effects of early life interventions on child adiposity, epigenetic mechanisms on assisted reproductive technology and infertility on child health, and environmental exposures in reproductive and child health.
The Bioinformatics Core integrates expertise in computer science with biology and mathematics to support analytics with large and/or multi-dimensional data. The Core comprises pipelines and advanced, biologically informed analytics for genomic, epigenomic, transcriptomic and metabolic data sets. The Bioinformatics Core includes capacity in multiple Artificial Intelligence approaches and is closely integrated with the Biostatistical and Data Management teams, as well as with multiple Singapore and international collaborative partners. The Core is developing capacity in knowledge transfer with modular bioinformatic tutorials targeting researchers across the basic and health sciences.
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