Institute of Information Science, Academia Sinica

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With approximately 10 million deaths in 2020, cancer continues to be the major cause for disease related mortality, worldwide. Biomedical research involving big data and high end computing solutions utilizing data science and machine learning technologies allows efficient unravelling of meaningful clinical and molecular consequences. Over the past decade we have been endeavoring into multifaceted research areas involving (i) development of computational algorithms and online analytical tools for interpreting high-throughput genomic data, (ii) characterization of high-throughput genomic profiles towards improved therapeutic strategies for cancers, and (iii) development of high-performance deep-learning pipelines for prediction and classification of cancer related attributes. MiRSystem, CellExpress, VariED and CNVIntegrate are online systems that enable cancer-big-data analysis and interpretation. Multiple prognostic expression signatures and scoring systems for various cancers (breast cancer and acute-myloid leukemia (AML)) have been proposed that distinguishes subtypes, and predicts disease risk and recurrence. Among others, noteworthy is a study, that established a novel prognostic marker NPM1 mutation, for AML that modulates miRNA-mRNA regulation. SEMA5A gene, was confirmed as a prognostic biomarker for non-small cell lung cancer (NSCLC), through a pioneering study that led to Semaphorin family be considered as a target for many national and international lung cancer studies. This further led to SEMA6A being established as a potential target for lung cancer immunotherapy.Last but not the least, various high-performing deep-learning pipelines for cancer subtype classification, risk prediction, and recurrence prediction using sequencing data, and image-only data have been successfully established. My research group has been contributing actively to cancer research for many years and is highly recognized among the academic community for our expertise, endeavours and interdisciplinary infrastructure.

Eric Y Chuang received his doctorate in cancer biology with toxicology and molecular genetics as two sub-specialties from Harvard University in 1997 and his doctoral thesis was to study radiation-induced mutagenesis in human cells. After graduation, he stayed at Harvard as a postdoctoral fellow for one year. He then joined the Radiation Biology Branch of National Cancer Institute (NCI), National Institutes of Health (NIH) as an IRTA fellow to study radiogenomics in Bethesda, MD, USA. Next, he became the Head of Microarray Laboratory for Radiation Oncology Sciences Program at NCI; his lab was to develop new initiatives that utilized state-of-the-art microarray technologies for studying radiation oncology related research projects. After working at the NIH for several years, he took a faculty position at National Taiwan University (NTU). In 2009, he joined the Radiation Research Program of Division of Cancer Treatment and Diagnosis at NCI as a Program Director to oversee a portfolio of NIH grants that included radiation-induced signaling pathways, molecular mechanisms and normal tissue injuries as well as radiation related genomic studies. In 2011, he returned to NTU and was serving as the Director of Graduate Institute of Biomedical Electronics and Bioinformatics (BEBI) in 2012-2018. Dr. Chuang received his Executive MBA degree in International Business Management from NTU in June 2017. Currently, he is a Professor of BEBI at NTU and serving as the Dean of Collage of Biomedical Engineering, China Medical University. Being an expert in genomic technologies, bioinformatics, cancer, radiation biology & oncology, biomedical engineering, and precision medicine, he has published more than 139 peer-reviewed papers in related fields. Moreover, Dr. Chuang has been serving as an editorial board member of Scientific Reports, and the Editor-in-Chief of Translation Cancer Research (http://tcr.amegroups.com/).