Using Machine Learning to Better Characterize and Understand the Clinical Prognostic Features and Landscape of Immunosequencing
About this Event
In recent decades, immunotherapy has been demonstrated as a significant clinical activity in cancer studies. T cells and B cells represent a crucial component of the adaptive immune system and are thought to mediate anti-tumoral immunity. Antigen-specific recognition by T cells is via the T cell receptor (TCR) which is unique for each T cell, clonotype, while B cells can also take up antigens with their BCRs to act as antigen-presenting cells (APC). Next generation sequencing (NGS) of the TCRs/BCRs can be used as a platform to profile the T/B cell repertoire. Though there are a number of software tools available, most of them are either designed for processing repertoire data by mapping antigen receptor segments to sequencing reads and assembling the clonotypes, or only examining the diversity of the TCR/BCR repertoire, they all lack of the ability to track and examine the dynamic nature of the repertoire across serial time points (the treatment effect) or correlate with clinical characteristics and clinical outcomes. We aim to develop customized statistical methods with advanced machine learning techniques to investigate the different aspects of TCR/BCR repertoire data in a clinical context to shift into a new era of classification and pattern recognition of immunosequencing. I will present 1) a comparative study of different feature selection (chi-square filter, correlated-based filter, support vector machine wrapper SVM-RFE, random forest wrapper Boruta), classification (SVM, random forest, bagging, boosting, RBF neural nets, MLP neural nets) and clustering methods (K-means, expectation-maximization); 2) a visualization pipeline of BCR affinity maturation while solving the computational burden by social network analysis.
Li Zhang, Ph.D., is an Associate Professor of Department Medicine and Department of Epidemiology and Biostatistics at University of California San Francisco (UCSF). She is an associate member and principle statistician of UCSF Helen Diller Family Comprehensive Cancer Center (HDFCCC) and faculty statistician for UCSF Clinical Translational Science Institute.
She obtained her Ph.D. in Statistics from University of Florida in 2006. She received pre-doctoral training from Division of Cancer Epidemiology and Genetics at NIH/NCI. Before she joined UCSF in 2013, she was an Assistant Professor at Cleveland Clinic and Case Western Reserve University for about 7 years. She is currently the Visiting Professor or Capital Medical University in Beijing, China.
Prof. Zhang has over fifteen years of experience in applying statistical method in biomedical research including basic science studies, epidemiology studies, clinical research. Her statistical methodological research interests are cancer epidemiology and immunoinformatics. She received multiple research rewards as a principal investigator (PI) and co-Investigator (Co-I), for example, she received UCSF Global Oncology Pilot Project Award on Genome Wide Association Study (GWAS) of Esophageal Cancer in Tanzania and recently received her second UCSF Research Allocation Program Award of Using Machine Learning to Better Characterize and Understand the Clinical Prognostic Features and Landscape of Immunosequencing. She received two Prostate Cancer Foundation Challenge Awards as statistical Co-I. She has more than 80 peer-reviewed publications and has been listed on more than 20 federal or foundation grants as a statistician.
“Human Augmentation: Blurring the line between Biology and Technology”
In recent lists of Emerging Technologies there is an ever increasing emphasis on “human related” technologies such as biochips, brain-computer interfaces, artificial tissue, the most prominent one being “Human Augmentation”.
Human Augmentation can be described as the natural, artificial, or technological alteration of the human body in order to enhance physical or mental capabilities. Many different forms of human enhancing technologies are either on the way or are currently being tested and trialed. A few of these emerging technologies include: human genetic engineering (gene therapy),
neurotechnology (neural implants and brain–computer interfaces), cyberware, nanomedicine, and 3D bioprinting. Physical enhancements include cosmetics (plastic surgery & orthodontics), Drug-induced (doping & performance-enhancing drugs), functional (prosthetics & powered exoskeletons), Medical (implants (e.g. pacemaker) & organ replacements. Computers, mobile phones, and Internet can also be used to enhance cognitive efficiency.
Dr. William Kao received his BSEE, MSEE and PhD from the University of Illinois Urbana-Champaign. He worked in the Semiconductor and Electronic Design Automation industries for more than 30 years holding several senior and executive engineering management positions at Texas Instruments, Xerox Corporation, and Cadence Design Systems.
【電磁波與健康】5G研討會 紀錄片《Generation Zapped》放映+座談Screening（Mandarin Event）
1:00pm Door open 開放入場
1:30pm Intro 開場
1:45pm Speech 演講
3:05pm Break 休息
3:15pm Screening 影片開始
4:30pm Panel Discussion (Mandarin) 座談
5:30pm Social and cleanup 交誼+清場
雙向影藝會社 Two-Way Theater (TWT)
Precision Medical Diagnostics – A Cross Disciplinary Application of Advanced Optical Sensor, Microfluidic, and Biochemistry
About this Event
A journey into bridges the gap between the technology and the medical application. In the case of a precision medical diagnostic device, a fluorescence bio-marker is developed by chemists and used to identify the target gene sequence, an bio-optical sensor is developed by IC designers and used for detecting the fluorescence light generate by the bio-market, a microfluidic chip is designed by fluidic experts for mixing the sample and reagents, and an analytical software is developed for generating final results for diagnosis by medical doctors. In this talk, we will walk through the technologies involved in a precision medical diagnostic device.
SVP & Cofounder
Anitoa Systems, LLC
Menlo Park, CA
Mr. Yuping Chung is the co-founder and SVP of Anitoa Systems LLC, a 6-year medical device and bio-sensor startup. Prior to Anitoa, Yuping served as General Manager and BU Director at Faraday Technology, Microchip, Macronix, Renesas, IDT, and others. Yuping also serves as advisor for startups in medical devices, datacenter persistent memory technologies, and industrial robotics. As a volunteer, Yuping is also served as Chairman and President of CASPA (Chinese American Semiconductor Professional Association) in 2014 and now serves as a member of Board of Advisors.