RESEARCH DATA INTEGRATION

Data science is an important component of biomedical and translational research, where data of multiple modalities are being constantly generated at unprecedented scale. The Research Data Integration group in the Biomedical Datahub Division aims to bridge the complexity of computational biology and data science with the needs of biologists and clinicians to drive biological discoveries and predict translational outcomes. To achieve this goal, we are developing capabilities to analyze and integrate multi-omics, imaging and clinical data generated by biomedical institutes in A*STAR, healthcare institutions and national platforms in Singapore to improve the usability and interpretability of large-scale multimodal datasets of cancer and other diseases.

Our group develops a wide range of computational, statistical, annotation and machine learning approaches to analyze large-scale multi-modal datasets generated from patients and patient derived models (xenografts, organoids and cell lines). We work closely with biologists, bioinformaticians and clinicians to build clinical and experimental data resources for diverse diseases that can scale research analysis and inform treatment options in the clinic, thereby improving patient outcomes.

Our focus is to manage, analyze, integrate and interpret large-scale biomedical and biological multi-modal data generated through large collaborative programs, to drive biomedical discoveries and scale data science efforts in Bioinformatics Institute. We are building data coordination centers and end-to-end data integration platforms for disease-related research programs, comprising multi-modal data. The novelty lies in bringing together and transforming Asian-centric datasets generated from multi-ethnic Singaporean patients across our local healthcare clusters, and ensuring sustainability for long term use. The strategic significance and impact of a centralized data effort can create synergy across multi-disciplinary collaborations to drive new discoveries for clinical translation and create harmonized datasets for future AI applications. We are driving, in partnership with clinical and wet-lab collaborators, high-impact scientific discoveries to address clinical unmet needs. Currently, our team comprise different computational expertise to build the data infrastructure that suits end-to-end data integration for data resources of different diseases and programs.

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