Signal Processing & Computational Biology
The Signal Processing and Computational Biology Group is an interdisciplinary research group in the Department of Electronic and Computer Engineering at The Hong Kong University of Science and Technology (HKUST). We focus on developing high dimensional statistical and machine learning methods, and applying them to problems in signal processing and computational biology. Our group also works on the theory and applications of random matrices
- Identification of SARS-derived B-cell and T-cell epitopes that provide vaccine target recommendations for SARS-CoV-2;
- For T cell epitopes, it reports estimated population coverage using HLA/MHC statistical information;
- Intuitive graphical interface with flexible parameter setting;
- Up-to-date reporting based on latest sequence data available (from GISAID).
An article related to the platform “COVIDep: a web-based platform for real-time reporting of vaccine target recommendations for SARS-CoV-2” has been published in Nature Protocols.
Highlighted on the cover page of Nature Protocols (July issue).
Related bioRxiv preprint: “COVIDep platform for real-time reporting of vaccine target recommendations for SARS-CoV-2: Description and connections with COVID-19 immune responses and preclinical vaccine trials“.
Saqib and Ahmed’s paper, “How genetic sequence data can guide vaccine design“, has been published in IEEE Potentials Magazine.
Neil, Faraz, and Awais shared their experience as international PhD students at HKUST in a Study International article.
February – March 2020
”Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies” has been published in Viruses on 25th Feb. 2020.
Cover article of Viruses (March issue).
Coverage in World Economic Forum article (by Prof. Wei Shyy, President of HKUST).
Coverage in the cover story (on COVID-19 vaccines) of New Scientist.
Congratulations to Muhammad Saqib Sohail for successfully defending his Ph.D.! Saqib’s thesis was entitled “A Stochastic Framework for Inference of Population Genetics Parameters from Time-series Genomic Data”. Saqib is going to continue his research as a post-doctoral fellow in our group.
- “RocaSec: A standalone GUI-based package for robust co-evolutionary analysis of proteins”. Joint with David Morales-Jimenez (Queens University, Belfast).
- “MPF-BML: A standalone GUI-based package for maximum entropy model inference”. Joint with Ray Louie (Kirby Institute, UNSW) and John Barton (University of California-Riverside).
Talk at 2018 Artificial Intelligence (AI) in Healthcare Summit, The Ritz Carlton, International Commerce Center, Hong Kong
Title: Inferring the fitness landscape of HIV by unsupervised learning
Speaker: Raymond Louie
Title: Using data science to find weak spots of Hepatitis C virus: Implications for rational vaccine design
Speaker: Matthew R. McKay
Local, mainland China, and general media
- South China Morning Post – Breakthrough HKUST-led research on mapping of HIV weak spots paves way for vaccine
- RTHK – Hong Kong Today (Big data analysis helping in development of HIV vaccine)
- World News – HKUST Researchers Discover Fitness Landscape of HIV Envelope Protein That May Help Vaccine Development
- Public Now
- Brinkwire – Big data methods learn the fitness landscape of the HIV Envelope protein
- Yahoo – 推算結果 實驗驗證 科大大數據分析病毒 助研發抗愛滋疫苗
- Eurekalert – Big data methods learn the fitness landscape of the HIV envelope protein
- Healthcare Analytics News – Big Data and Machine Learning Take on HIV
- Science Daily – Big data methods learn the fitness landscape of the HIV Envelope protein
- Medical Express – Big data methods applied to the fitness landscape of the HIV envelope protein
— Banner photo taken by HKUST alumnus Terence Pang