アブストラクト
| Date | 14:00-15:00 Aug 01, 2023 |
| Speaker | Chun-Yu Lin Assisstant Professor, Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Taiwan |
| Title | Single-sample networks for deciphering individual features in disease |
| Abstract |
Recently, extracting inherent biological system information (e.g., cellular networks) from genome-wide expression profiles for developing
personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample
networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we present a sample-specific
weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e., sample weights) with
the differential network between perturbed and aggregate networks [1]. Integrating SWEET SINs with a network proximity measure facilitates
characterizing individual features and therapy in diseases, such as identifying repurposable drugs and predicting drug sensitivity in
specific cancer cell lines. Moreover, the SWEET method can not only effectively discriminate different cancer types (or cell line types) but
also identify two potential subtypes of lung adenocarcinoma with distinguishing clinical features. In this talk, I will introduce currently used SIN inference methods and how these methods compensate for the current analysis of omics data and offer a view of biological network systems to enable the development of next-generation personalized medicine and clinical decision support systems.
[1] Chen, H. H.#, Hsueh, C. W. #, Lee, C. H. #, Hao, T. Y. #, Tu, T. Y., Chang, L. Y., Lee, J. C., Lin, C. Y.* (2023). SWEET: a single-sample network inference method for deciphering individual features in disease. Briefings in Bioinformatics, 24(2), bbad032.
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