X
Xin Gao
Researcher at King Abdullah University of Science and Technology
Publications - 359
Citations - 7859
Xin Gao is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 38, co-authored 330 publications receiving 5388 citations. Previous affiliations of Xin Gao include Carnegie Mellon University & Hong Kong University of Science and Technology.
Papers
More filters
Journal ArticleDOI
SARS-CoV-2 induced diarrhoea as onset symptom in patient with COVID-19.
TL;DR: It is concluded that patients with GIT symptoms should not be ignored for the virus infectivity, especially during the outbreak period, as the coronavirus disease 2019 (COVID-19) beginning in Wuhan has rapidly spread around China and other countries.
Journal ArticleDOI
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.
TL;DR: This review provides both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics, and introduces deep learning in an easy-to-understand fashion.
Journal ArticleDOI
DEEPre: sequence-based enzyme EC number prediction by deep learning
TL;DR: This paper proposes an end‐to‐end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction, which improves the prediction performance over the previous state‐of‐the‐art methods.
Journal ArticleDOI
Bioinformatics clouds for big data manipulation.
TL;DR: This article reviews extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a service (SaaS%), Platform as aservice (PaaS) and Infrastructure as aService (IaaS); and presents perspectives on the adoption of cloud computing in bio informatics.
Journal ArticleDOI
A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis
Longxi Zhou,Zhongxiao Li,Juexiao Zhou,Haoyang Li,Yupeng Chen,Yuxin Huang,Dexuan Xie,Lintao Zhao,Ming Fan,Shahrukh K. Hashmi,Faisal Abdelkareem,Riham Eiada,Xigang Xiao,Lihua Li,Zhaowen Qiu,Xin Gao +15 more
TL;DR: A fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources is proposed and its important application value in combating the disease is suggested.