HKUEMS :: Event Details

50th Anniv. Seminar Series on 'Identifying biomarker signatures for ...' by Professor Yuanjia WANG
posted by Department of Statistics and Actuarial Science for HKU and Public
Event Type: Public Lecture/Forum/Seminar/Workshop/Conference/Symposium
Event Nature: Science & Technology

Event Details

DEPARTMENT OF STATISTICS AND ACTUARIAL SCIENCE
THE UNIVERSITY OF HONG KONG

50th Anniversary Seminar Series

Professor Yuanjia WANG
Department of Biostatistics, Mailman School of Public Health Columbia University
Division of Biostatistics, New York Psychiatric Institute USA

will give a talk

entitled

IDENTIFYING BIOMARKER SIGNATURES FOR
NEURODEGENERATIVE DISEASES FROM LARGE-SCALE
BIOMARKER MEASURES WITH NETWORK STRUCTURE

Abstract

Potential disease-modifying therapies for neurodegenerative disorders need to be introduced prior to the symptomatic stage in order to be effective. However, current diagnosis of neurological disorders mostly rely on measurements of clinical symptoms and thus only identify symptomatic subjects in their late disease course. Thus, it is of interest to select and integrate biomarkers that may reflect early disease-related pathological changes for earlier diagnosis and recruiting pre-sypmtomatic subjects in a prevention clinical trial. In many clinicial studies of neurological disorders, researchers collect measurements of both static and dynamic biomarkers over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Leveraging all available, infrequently measured dynamic biomarkers to improve prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation and accommodate network structure among biomarkers, and an efficient augmented penalization minimization algorithm is adopted for computation. We apply the proposed method to a recently completed natural history study of Huntington's disease (HD) to predict time to disease conversion using structural change at huntingtin gene and longitudinal, whole brain structural magnetic resonance imaging biomarkers. Lastly, we discuss an approach to estimate causal networks using high-dimensional biomarkers with an application to discover protein signaling network from human immune T-cell data and to HD data for constructing brain atrophy network.

Date/Time18/07/2017 11:00-12:00
VenueRoom 301, Run Run Shaw Building, HKU
LanguageEnglish
Programmeclick to view
10:45 to 11:00 Refreshments will be served outside Room 301 Run Run Shaw Building
11:00 to 12:00 Seminar

Registration Instruction

Registration is not required.

Contact Information

For further information, please visit:
http://www.saasweb.hku.hk/seminar/seminar.php

Should you have any enquiries, please feel free to contact Ms. Irene Cheung by email at saas@hku.hk or by phone at 39173812 or by fax at 28589041.