Biology subjects

Yeoh, E.-k.

Publications and source records attributed to Yeoh, E.-k..

3 recordsLinked to original sources

bioRxiv2023

Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations

A predictive evolutionary model was developed to forecast representative influenza viral strains and select vaccine strains for upcoming epidemic seasons. Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. A computational approach beth-1 that models site-wise mutation dynamics demonstrated remarkable matching of predicted strains to the circulating viruses in subsequent seasons for the influenza A(H1N1)pdm09 and A(H3N2) viruses in both retrospective and prospective validations. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus, achieved by capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response. One-Sentence SummaryA computational model predicts virus evolution and facilitates vaccine strain selection for the influenza virus

evolutionary biology↗

bioRxiv2020

Characterization of the evolutionary dynamics of influenza A H3N2 hemagglutinin

Virus evolution drives the annual influenza epidemics in human population worldwide. However, it has been challenging to evaluate the mutation effect of the influenza virus on evading the population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics by the effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by the effective mutation periods (EMPs). Extensive analysis is performed on the genome and epidemiology data of 13-year worldwide H3N2 epidemics involving nine regions in four continents. We showed that the identified EM processed similar profile in geographically adjacent regions, while only 40% are common to Europe, North America, Asia and Oceania, indicating that the regional specific mutations also contributed significantly to the global H3N2 epidemics. The mutation dynamics calibrated that around 90% of the common EMs underlying global epidemics were originated from South East Asia, led by Thailand and India, and the rest were originated from North America. New Zealand was found to be the dominate sink region of H3N2 circulation, followed by UK. All regions might act as the intersection in the H3N2 transmission network. The proposed methodology provided a way to characterize key amino acids from the genetic epidemiology point of view. This approach is not restricted by the genomic region or type of the virus, and will find broad applications in identifying therapeutic targets for combating infectious diseases.

evolutionary biology↗

bioRxiv2019

Tensor Factorization-based Prediction with an Application to Estimating the Risk of Chronic Diseases

Tensor factorization has emerged as a powerful method to address the challenges of high dimensionality regarding disease development and comorbidity. Chronic diseases have a high likelihood to co-occur, making patients suffering from one chronic disease to have an elevated risk for the other diseases in the course of aging. Individualized prediction of chronic diseases can help patients prevent new diseases and reduce the healthcare costs. Despite rich results of risk assessment models for chronic diseases, individualized risk prediction considering the complex mechanisms of disease development and comorbidity remains to be under-researched. This research aims to develop tensor factorization-based machine learning models to predict the onset of new chronic diseases for individual patients through incorporating the comorbidity patterns with the clinical and sequential factors revealed in the electronic health records (EHR) data. We propose two tensor factorization-based methods to incorporate the clinical and sequential factors to reveal the latent patterns of co-occurring chronic diseases. The efficacy of the proposed methods was validated through predicting the onset of new chronic diseases for individual patients using the EHR data for 23 years from a major hospital in Hong Kong. The proposed methods consistently outperform benchmark predictive models. The top 10 predictions of new chronic diseases have approximately 60% recall. Tensor factorization is an appropriate method for predicting the onset of chronic diseases at the individual level. The proposed predictive models could inform proactive health management programs for at-risk patients with different chronic conditions at discharge.\n\nAuthor summaryThe existing risk assessment models mainly focused on the prediction of single diseases in the population base. Chronic disease risk prediction considering the complex mechanisms of disease development and comorbidity is under-researched. To support and inform clinical decision making for healthcare professionals in the aging society, this study provides an innovative approach to mapping an interconnected web of chronic illnesses and investigated the performance of chronic disease prediction using 2 years worth of patient assessment records and 23 years admission history data from a major hospital in Hong Kong. We proposed matrix and tensor-based methods to represent the high-order interrelations of patients, chronic diseases and additional features, which can reveal the latent patterns of co-occurring chronic diseases to enable more effective prediction. The proposed methods exhibit state-of-the-art performance in predicting the onset of new chronic diseases for individual patients.

bioinformatics↗