World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
14415
World Ranking
2778
National Ranking
378

Overview

Xuan Xiao is affiliated with the Jingdezhen Ceramic Institute in China and works primarily in the field of Engineering. Their research spans several subfields, including Electrical and Electronic Engineering, Biomedical Engineering, Molecular Biology, Statistics and Probability, and Mechanical Engineering.

Their recent publications include:

  • A single-atom manganese nanozyme mediated membrane reactor for water decontamination, 2024, Water Research
  • CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression, 2020, Scientific Reports
  • Perfect light absorption in monolayer MoS2 empowered by optical Tamm states, 2021, Chinese Optics Letters
  • LncRNA gadd7 promotes mitochondrial membrane potential decrease and apoptosis of alveolar type II epithelial cells in hyperoxia-induced lung injury by promoting MFN1, 2023, European Journal of Histochemistry
  • Relay Power Allocation for NAF Cooperation Assisted NOMA Network, 2020, IEEE Wireless Communications Letters

Xiao's frequent coauthors include Jianlin Zhao, Guoyue Liu, Cunzhi Yin, Hang Wu, and Jin-Hong Lin, each having collaborated on multiple projects.

The scientist has published regularly in venues such as PubMed, Water Research, Scientific Reports, Chinese Optics Letters, and the European Journal of Histochemistry.

Xiao's main topics of work cover a diverse array of research interests:

  • Photonic and Optical Devices
  • Cancer-related molecular mechanisms research
  • Mitochondrial Function and Pathology
  • Fluorine in Organic Chemistry
  • Advanced Nanomaterials in Catalysis
  • Electrochemical sensors and biosensors
  • Advanced Photocatalysis Techniques

Best Publications

  • pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach

    Jianhua Jia;Jianhua Jia;Zi Liu;Xuan Xiao;Bingxiang Liu

  • iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components

    Wang Ren Qiu;Xuan Xiao;Kuo Chen Chou

  • iPTM-mLys: identifying multiple lysine PTM sites and their different types

    Wang-Ren Qiu;Bi-Qian Sun;Xuan Xiao;Zhao-Chun Xu

  • iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals.

    Xiang Cheng;Xiang Cheng;Shu-Guang Zhao;Xuan Xiao;Kuo-Chen Chou;Kuo-Chen Chou

  • iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC.

    Jianhua Jia;Zi Liu;Xuan Xiao;Bingxiang Liu

  • iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

    Jianhua Jia;Zi Liu;Xuan Xiao;Bingxiang Liu

  • pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC

    Xiang Cheng;Xuan Xiao;Kuo-Chen Chou

  • iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

    Wang-Ren Qiu;Shi-Yu Jiang;Zhao-Chun Xu;Xuan Xiao

  • iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach.

    Xuan Xiao;Jian-Liang Min;Wei-Zhong Lin;Zi Liu

  • iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach.

    Wang-Ren Qiu;Xuan Xiao;Wei-Zhong Lin;Kuo-Chen Chou

  • pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC

    Xiang Cheng;Xuan Xiao;Kuo-Chen Chou

  • pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.

    Xiang Cheng;Xiang Cheng;Shu-Guang Zhao;Wei-Zhong Lin;Xuan Xiao

  • iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model.

    Wang-Ren Qiu;Xuan Xiao;Wei-Zhong Lin;Kuo-Chen Chou

  • pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

    Jianhua Jia;Liuxia Zhang;Zi Liu;Xuan Xiao

  • iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.

    Jianhua Jia;Zi Liu;Xuan Xiao;Bingxiang Liu

  • pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

    Xiang Cheng;Xuan Xiao;Kuo-Chen Chou

  • pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC.

    Xiang Cheng;Xuan Xiao;Kuo-Chen Chou;Kuo-Chen Chou

  • iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.

    Wang-Ren Qiu;Bi-Qian Sun;Xuan Xiao;Zhao-Chun Xu

  • iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier.

    Wang-Ren Qiu;Bi-Qian Sun;Xuan Xiao;Zhao-Chun Xu

  • pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

    Xiang Cheng;Xuan Xiao;Kuo-Chen Chou;Kuo-Chen Chou

  • iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.

    Wang-Ren Qiu;Xuan Xiao;Zhao-Chun Xu;Kuo-Chen Chou

Frequent Co-Authors

Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Dong Xu
Dong Xu University of Missouri
Licheng Jiao
Licheng Jiao Xidian University
Dong-Jun Yu
Dong-Jun Yu Nanjing University of Science and Technology

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