Z
Zhongxiao Li
Researcher at King Abdullah University of Science and Technology
Publications - 15
Citations - 511
Zhongxiao Li 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 4, co-authored 7 publications receiving 246 citations.
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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.
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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.
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Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets
TL;DR: How in-silico drug repurposing has the promise to shortly improve the authors' arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology is discussed.
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DeeReCT-PolyA: A robust and generic deep learning method for PAS identification
TL;DR: It is shown that the single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets.
Journal ArticleDOI
DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.
Zhongxiao Li,Yisheng Li,Bin Zhang,Yu Li,Yongkang Long,Juexiao Zhou,Xudong Zou,Min Zhang,Yuhui Hu,Wei Chen,Xin Gao +10 more
TL;DR: DeeReCT-APA as discussed by the authors treats the problem as a regression task with a variable-length target and uses bidirectional LSTM to explicitly model the interactions among competing PASs.