“Data analytics for precision medicine”

Abstract
Precision medicine is the talk of the town. The idea aims to fine-tune each individual patient's therapy based on their unique individuality. This individuality comes from their phenotypic data as well as their genetic information. Two obvious areas of immediate impact of precision medicine are congenital rare diseases and somatically acquired cancers. In this talk I will share some of our recent efforts in both. For rare diseases, often enough an individual condition is only observed in a handful of patients (n=1 being the extreme). This pushes the onus on prior knowledge integration in identifying the culprit gene. Our efforts in this domain have led to the implication of dysfunction of SMCHD1 for Arhhinia (a neonatal condition reported in about 100 plus cases in history), and dysfunction of ALPK1 for ROSAH (a progressive condition observed in a handful of families to date). For cancer patients, often enough each individual tumour follows a unique evolutionary trajectory. The challenge is to understand where it's heading to ward off its progression. I will share how integrating multiple omics patients readouts helped us lead to better understanding of chronic myeloid leukaemia progression. Big picture, I will share therapeutic implications of each of these findings. Of note, these efforts are in active collaboration with clinical and experimental scientists. Time permitting I will discuss some of our other ongoing projects as well.

Bio
Asif Javed is an Assistant Professor (Research) in the school of Biomedical Sciences at the University of Hong Kong. His research focuses on computational biology with an emphasis on clinical applications. He has co-authored 50+ publications including high impact journals like Nature Methods and Nature Genetics.