Yukui Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Yukui Zhang has a total of 132 co-authors. Here are the top co-authors by number of shared publications:
- Langxing Chen
College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Nankai University, Tianjin 300071, China
31 shared publications Xiwen He
College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Nankai University, Tianjin 300071, China
20 shared publications- Wen-You Li
Nankai University
18 shared publications Xi-Wen He
State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China
16 shared publications- Xiwen He
College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Nankai University, Tianjin 300071, China
12 shared publications - Yijun Li
College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Nankai University, Tianjin 300071, China
7 shared publications
Latest Publications
- 21 June 2026
- Published by Elsevier in Journal of Hazardous Materials
- Vol. 514, 142784
Sensitive Detection of PFOA in Environmental Water Based on Pt/MnO2 Nanozymes with Engineered d-Band Structure
- 21 June 2026
- Published by Elsevier in Journal of Hazardous Materials
- Vol. 514, 142784
Sensitive Detection of PFOA in Environmental Water Based on Pt/MnO2 Nanozymes with Engineered d-Band Structure
- 10 April 2026
- Published by Elsevier in Journal of Colloid and Interface Science
- Vol. 716, 140430
Corrigendum to “Responsive molecularly imprinted nanozyme with H2O2 self-supply cascade catalytic system for targeted enhancement of tumor chemodynamic therapy” [J. Colloid Interface Sci. 707 (2026) 139697]
- 10 April 2026
- Published by Elsevier in Journal of Colloid and Interface Science
- Vol. 716, 140430
Corrigendum to “Responsive molecularly imprinted nanozyme with H2O2 self-supply cascade catalytic system for targeted enhancement of tumor chemodynamic therapy” [J. Colloid Interface Sci. 707 (2026) 139697]
- 21 March 2026
- Published by Elsevier in Food Chemistry
- Vol. 512, 148989
In-situ grown dual lanthanide metal-organic framework electrospinning nanofiber film for the ratiometric fluorescent sensing of glyphosate in foods
- 21 March 2026
- Published by Elsevier in Food Chemistry
- Vol. 512, 148989
In-situ grown dual lanthanide metal-organic framework electrospinning nanofiber film for the ratiometric fluorescent sensing of glyphosate in foods
- 22 February 2026
- Published by Elsevier in Food Chemistry
- Vol. 509, 148553
In situ-grown ambient-temperature dual-emissive RhB/COF@ZIF-90 composite for ratiometric fluorescence detection of tetracycline in foods
- 22 February 2026
- Published by Elsevier in Food Chemistry
- Vol. 509, 148553
In situ-grown ambient-temperature dual-emissive RhB/COF@ZIF-90 composite for ratiometric fluorescence detection of tetracycline in foods
- 14 December 2025
- Published by Elsevier in Journal of Colloid and Interface Science
- Vol. 707, 139697
Responsive molecularly imprinted nanozyme with H2O2 self-supply cascade catalytic system for targeted enhancement of chemodynamic therapy
- 14 December 2025
- Published by Elsevier in Journal of Colloid and Interface Science
- Vol. 707, 139697
Responsive molecularly imprinted nanozyme with H2O2 self-supply cascade catalytic system for targeted enhancement of chemodynamic therapy
- 31 December 2024
- Published by Elsevier in Journal of Colloid and Interface Science
- Vol. 683 (Pt 2) , 890-905
Dual-template epitope imprinted nanoparticles for anti-glycolytic tumor-targeted treatment
- 31 December 2024
- Published by Elsevier in Journal of Colloid and Interface Science
- Vol. 683 (Pt 2) , 890-905
Dual-template epitope imprinted nanoparticles for anti-glycolytic tumor-targeted treatment
- Open Access
- 22 November 2024
- Published by Oxford University Press (OUP) in Briefings in Bioinformatics
- Vol. 26 (1)
Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach
Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from health controls. Using plasma peptides, the neural network, termed as CMS-Net, can perform diagnosis and prediction with an accuracy of 0.9441. The sensitivity and specificity reached 0.9352 and 0.9517 respectively, and the area under the curve was enhanced to 0.9634. Using the gradient-based feature importance method to interpret crucial features, we identify 28 differential peptide sequences from 14 precursor proteins (e.g. hemoglobin, immunoglobulin, albumin, etc.). This work highlights the possibility of molecular diagnosis of MDD with the aid of chemical and computer science.
- 22 November 2024
- Published by Oxford University Press (OUP) in Briefings in Bioinformatics
- Vol. 26 (1)
Open AccessToward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach
Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from health controls. Using plasma peptides, the neural network, termed as CMS-Net, can perform diagnosis and prediction with an accuracy of 0.9441. The sensitivity and specificity reached 0.9352 and 0.9517 respectively, and the area under the curve was enhanced to 0.9634. Using the gradient-based feature importance method to interpret crucial features, we identify 28 differential peptide sequences from 14 precursor proteins (e.g. hemoglobin, immunoglobulin, albumin, etc.). This work highlights the possibility of molecular diagnosis of MDD with the aid of chemical and computer science.