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Music making from biomarkers to functional patient profile: a way leading to personalized medicine?
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报告题目:Music making from biomarkers to functional patient profile: a way leading to personalized medicine?

报告人: 何雪辉, 研究员/Data Scientist, 内梅亨大学拉德堡医学院

照片:

邀请人:刘振华

报告时间:2019年7月8日14:30-16:00

报告地点:南校区信远楼2区206报告厅

报告人简介:何雪辉,女,博士后,内梅亨大学拉德堡医学院(Radboudumc)研究员。2007年获得德国蒂宾根大学医学院(Tuebingen University Medical Centre)理学博士学位,2008年至今在内梅亨大学拉德堡医学院工作。她主要从事免疫稳态及免疫调控方面的基础研究;诱发免疫耐受性在自发性免疫疾病和器官移植中的临床应用研究;银屑病及癌症患者在接受免疫治疗方案后的实时监控数据分析等。何研究员是荷兰免疫协会及中国免疫协会会员,担任Frontiers in Immunology,Immunotheropies 等期刊和会议审稿人,发表SCI 论文20余篇,他引300余次。

报告摘要:

If we want to deliver on personalized medicine, we need to develop new forms of diagnostics and improve our way to process data. But how do we move from single marker to pattern recognition profiling? It requires the new ways of processing, analysing and visualizing dynamic patient data. Ital so needs the proper statistical tools for the interpretation of such large-scale biological data. From the immunological point of view, the principle of novel diagnostic tools includes the phenotype measurements of immune cells, phosphorylation status detection of cell signal transduction pathways, and the functional profiling and evaluation. Interpretation of modern biology, which is a data-rich science, driven by our ability to measure the detailed molecular characteristics of cells, organs, and individuals at many different levels, requires the detection of statistical dependencies and patterns in order to establish useful models of complex biological systems. Techniques from machine learning are key in this endeavour. Typical examples are the visualization of high-throughput (flow cytometric) data using dimensionality reduction methods, as well as classification and neural-network based model establishing. Here several examples will be discussed to show that standardization of data structure makes processing of data more easy, use of Shiny-server (R) gives access and real-time viewing of data, patient cohorts can be easily compared and classified. By using the immunephenotyping and the functional phosphoproteome phenotype of patients in clinical context, we expect to evaluate the effect of medication on identified profiles. Further data integration and processing moving forward with pattern recognition profiling is our final goal to realize.

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