• Digital-Health-Conference-2020

Invited SpeakersProfile Details

Yu Li (Ph.D. student)
Yu Li (Ph.D. student) Yu Li is a Ph.D. student of KAUST, majoring in Computer Science (CS), under the supervision of Prof. Xin Gao


Yu Li is a Ph.D. student of KAUST, majoring in Computer Science (CS), under the supervision of Prof. Xin Gao. He is a member of Computational Bioscience Research Center (CBRC). His main research interest is developing novel computational methods and algorithms, mainly deep learning methods, for solving biological problems and understanding the principles behind the bio-world. He obtained MS degree in CS from KAUST in 2016. Before that, he got the Bachelor degree in Bioscience from University of Science and Technology of China (USTC). 

All sessions by Yu Li (Ph.D. student)

  • Day 3Wednesday, January 22nd
Session 6 : Spotlight on Young Talent (Chair Prof. Robert Hoehndorf)
1:20 pm

Assist antibiotic resistance detection with deep learning

Antibiotic resistance has become one of the most urgent threats to global health, as more drugs are losing sensitivity to bacterium they were designed to kill. When investigating it, researchers usually need to identify and annotate antibiotic resistance genes (ARGs) from environmental or clinical samples.

Despite the existence of several computational tools for performing ARG annotation, most of them rely on sequence alignment, which can result in false negatives and biased prediction because of the incompleteness of the databases. In addition, most existing computational tools provide no information about the mobility of genes and the underlying mechanisms of resistance.

To address such limitations, we propose an end-to-end Hierarchical Multi-task Deep learning framework for Antibiotic Resistance Gene annotation(HMD-ARG), taking raw sequence encoding as input and then annotating ARGs sequences from three aspects: resistant drug type, the underlying mechanism of resistance, and gene mobility. HMD-ARG can potentially serve as a tool for detecting antibiotic resistance with high accuracy.

Building 19, Hall 1 13:20 - 13:40 Details