B1916
Title: Disease prediction by detecting and integrating connectomic networks and marginally weak signals
Authors: Yanming Li - University of Kansas Medical Center (United States) [presenting]
Abstract: Many contemporary studies use individual genomic or imaging profiles for early prediction of cancer or neuropsychological outcomes, such as cancer subtypes and Alzheimer's disease stages. Current approaches ignore the connection structures of the genome and the brain (e.g. gene pathways or brain networks). Despite having marginally weak effects, many genetic and imaging markers may exude strong predictive effects once considered together with their connected biomarkers. To find such weak signals, the inter-feature connectomic structure of the genome or brain must be explored first. However, given the ultrahigh-dimensional characteristic of genomic/neuroimaging profiles, identifying the whole genome/brain connectomic features is computationally prohibitive. This is also an impediment to detecting weak signals. We hypothesize that a large portion of the predictiveness of disease outcomes is attributed to inter-marker connections and marginally weak signals. By detecting and integrating them, prediction accuracy can be significantly improved. We develop novel statistical/machine-learning algorithms for detecting network-based biomarkers for cancer or AD-related outcome prediction. The identified network signatures and weak signals will also enhance our understanding of the underlying mechanisms of disease development and progression.
