Works by Liu, Guixia
Results: 226
GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction**.
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- Molecular Informatics, 2025, v. 44, n. 1, p. 1, doi. 10.1002/minf.202400169
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Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins.
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- BMC Bioinformatics, 2025, v. 26, n. 1, p. 1, doi. 10.1186/s12859-025-06093-5
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Integrated transcriptomic and metabolomic analyses reveal the molecular mechanism of flower color differentiation in Orychophragmus violaceus.
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- Frontiers in Plant Science, 2025, p. 1, doi. 10.3389/fpls.2025.1509120
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Flexible Janus Nanoribbons Array: A New Strategy to Achieve Excellent Electrically Conductive Anisotropy, Magnetism, and Photoluminescence.
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- Advanced Functional Materials, 2015, v. 25, n. 16, p. 2436, doi. 10.1002/adfm.201500348
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Magnetism and white-light-emission bifunctionality simultaneously assembled into flexible Janus nanofiber via electrospinning.
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- Journal of Materials Science, 2015, v. 50, n. 24, p. 7884, doi. 10.1007/s10853-015-9313-5
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Coaxial nanofibers to help achieve tunable and enhanced simultaneous magnetic-luminescent bifunctionality.
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- Journal of Materials Science, 2015, v. 50, n. 4, p. 1679, doi. 10.1007/s10853-014-8729-7
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Flexible Janus nanofiber to acquire tuned and enhanced simultaneous magnetism-luminescence bifunctionality.
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- Journal of Materials Science, 2014, v. 49, n. 20, p. 7244, doi. 10.1007/s10853-014-8431-9
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A new strategy to assemble enhanced magnetic-photoluminescent bifunction into a flexible nanofiber.
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- Journal of Materials Science, 2014, v. 49, n. 15, p. 5418, doi. 10.1007/s10853-014-8253-9
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Synthesis and luminescence properties of Yb-Er co-doped LaOCl nanostructures.
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- Journal of Materials Science, 2014, v. 49, n. 7, p. 2919, doi. 10.1007/s10853-013-8003-4
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Parallel spinnerets electrospinning construct and properties of electrical-luminescent bifunctional bistrand-aligned nanobundles.
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- Journal of Materials Science, 2014, v. 49, n. 5, p. 2171, doi. 10.1007/s10853-013-7910-8
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Fabrication and luminescence properties of YF:Eu hollow nanofibers via coaxial electrospinning combined with fluorination technique.
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- Journal of Materials Science, 2013, v. 48, n. 17, p. 5930, doi. 10.1007/s10853-013-7388-4
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Electrospinning preparation of LaOBr:Tb nanostructures and their photoluminescence properties.
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- Journal of Materials Science, 2013, v. 48, n. 6, p. 2557, doi. 10.1007/s10853-012-7046-2
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Synthesis of YOS:Eu luminescent nanobelts via electrospinning combined with sulfurization technique.
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- Journal of Materials Science, 2013, v. 48, n. 2, p. 644, doi. 10.1007/s10853-012-6768-5
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Electrospinning preparation and photoluminescence properties of Y<sub>3</sub>Al<sub>5</sub>O<sub>12</sub>:Tb<sup>3+</sup> nanostructures.
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- Luminescence: Journal of Biological & Chemical Luminescence, 2015, v. 30, n. 6, p. 751, doi. 10.1002/bio.2816
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Single flexible nanofiber to achieve simultaneous photoluminescence-electrical conductivity bifunctionality.
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- Luminescence: Journal of Biological & Chemical Luminescence, 2015, v. 30, n. 1, p. 26, doi. 10.1002/bio.2684
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Preparation and up-conversion luminescence properties of LaOBr:Yb<sup>3+</sup>/Er<sup>3+</sup> nanofibers via electrospinning.
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- Luminescence: Journal of Biological & Chemical Luminescence, 2014, v. 29, n. 7, p. 908, doi. 10.1002/bio.2640
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Amphipathic Janus Nanofibers Aerogel for Efficient Solar Steam Generation.
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- Energy & Environmental Materials, 2024, v. 7, n. 4, p. 1, doi. 10.1002/eem2.12667
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Network-Based Methods for Prediction of Drug-Target Interactions.
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- Frontiers in Pharmacology, 2018, p. N.PAG, doi. 10.3389/fphar.2018.01134
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Computational Insight Into Vitamin K<sub>1</sub> ω-Hydroxylation by Cytochrome P450 4F2.
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- Frontiers in Pharmacology, 2018, p. N.PAG, doi. 10.3389/fphar.2018.01065
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A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer’s Disease.
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- Frontiers in Pharmacology, 2018, p. N.PAG, doi. 10.3389/fphar.2018.00668
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Identification of Nontoxic Substructures: A New Strategy to Avoid Potential Toxicity Risk.
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- Toxicological Sciences, 2018, v. 165, n. 2, p. 396, doi. 10.1093/toxsci/kfy146
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Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.
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- Journal of Applied Toxicology, 2024, v. 44, n. 7, p. 1050, doi. 10.1002/jat.4601
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In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.
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- Journal of Applied Toxicology, 2024, v. 44, n. 6, p. 892, doi. 10.1002/jat.4586
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In silico prediction of hERG blockers using machine learning and deep learning approaches.
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- Journal of Applied Toxicology, 2023, v. 43, n. 10, p. 1462, doi. 10.1002/jat.4477
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In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches.
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- Journal of Applied Toxicology, 2022, v. 42, n. 11, p. 1766, doi. 10.1002/jat.4354
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In silico prediction of potential drug-induced nephrotoxicity with machine learning methods.
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- Journal of Applied Toxicology, 2022, v. 42, n. 10, p. 1639, doi. 10.1002/jat.4331
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In Silico prediction of inhibitors for multiple transporters via machine learning methods.
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- Molecular Informatics, 2024, v. 43, n. 3, p. 1, doi. 10.1002/minf.202300270
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In silico prediction of drug‐induced liver injury with a complementary integration strategy based on hybrid representation.
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- Molecular Informatics, 2023, v. 42, n. 7, p. 1, doi. 10.1002/minf.202200284
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Drug Repurposing for Newly Emerged Diseases via Network‐based Inference on a Gene‐disease‐drug Network.
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- Molecular Informatics, 2022, v. 41, n. 9, p. 1, doi. 10.1002/minf.202200001
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In Silico Prediction of Metabolic Epoxidation for Drug‐like Molecules via Machine Learning Methods.
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- Molecular Informatics, 2020, v. 39, n. 8, p. 1, doi. 10.1002/minf.201900178
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In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method.
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- Molecular Informatics, 2016, v. 35, n. 3/4, p. 136, doi. 10.1002/minf.201500055
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Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow.
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- Molecular Informatics, 2015, v. 34, n. 10, p. 679, doi. 10.1002/minf.201400119
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In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods.
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- Molecular Informatics, 2015, v. 34, n. 4, p. 228, doi. 10.1002/minf.201400127
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Computational Insights into Ligand Selectivity of Estrogen Receptors from Pharmacophore Modeling.
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- Molecular Informatics, 2011, v. 30, n. 6/7, p. 539, doi. 10.1002/minf.201000170
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Geochemical characteristics of natural gas from mud volcanoes in the southern Junggar Basin.
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- SCIENCE CHINA Earth Sciences, 2012, v. 55, n. 3, p. 355, doi. 10.1007/s11430-012-4363-x
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Co/Fe Dual Catalysis for Sequential Hydrosilylation–Isomerization: Access to Trisubstituted (E)‐Alkenyl Silanes from Terminal Alkynes.
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- Chinese Journal of Chemistry, 2024, v. 42, n. 16, p. 1860, doi. 10.1002/cjoc.202400291
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Thermocatalytic Dehydrogenation‐Enabled Arene‐Alkane Couplings<sup>†</sup>.
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- Chinese Journal of Chemistry, 2023, v. 41, n. 16, p. 1905, doi. 10.1002/cjoc.202300220
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Dehydrogenation Based Asymmetric Epoxidation of Arylalkanes to Chiral Epoxides.
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- Chinese Journal of Chemistry, 2022, v. 40, n. 19, p. 2263, doi. 10.1002/cjoc.202200304
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Asymmetric Transfer Hydrogenation of Diaryl Ketones with Ethanol Catalyzed by Chiral NCP Pincer Iridium Complexes.
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- Chinese Journal of Chemistry, 2022, v. 40, n. 10, p. 1131, doi. 10.1002/cjoc.202100868
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Iron Catalyzed Isomerization of α‐Alkyl Styrenes to Access Trisubstituted Alkenes.
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- Chinese Journal of Chemistry, 2021, v. 39, n. 3, p. 585, doi. 10.1002/cjoc.202000492
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The role of benzoic acid in proline-catalyzed asymmetric michael addition: A density functional theory study.
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- International Journal of Quantum Chemistry, 2013, v. 113, n. 9, p. 1339, doi. 10.1002/qua.24297
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MetaADEDB 2.0: a comprehensive database on adverse drug events.
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- Bioinformatics, 2021, v. 37, n. 15, p. 2221, doi. 10.1093/bioinformatics/btaa973
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admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties.
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- Bioinformatics, 2019, v. 35, n. 6, p. 1067, doi. 10.1093/bioinformatics/bty707
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In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning.
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- Toxicology Research, 2019, v. 8, n. 3, p. 341, doi. 10.1039/c8tx00331a
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In silico prediction of chemical genotoxicity using machine learning methods and structural alerts.
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- Toxicology Research, 2018, v. 7, n. 2, p. 211, doi. 10.1039/c7tx00259a
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In silico prediction of pesticide aquatic toxicity with chemical category approaches.
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- Toxicology Research, 2017, v. 6, n. 6, p. 831, doi. 10.1039/c7tx00144d
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In silico prediction of hERG potassium channel blockage by chemical category approaches.
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- Toxicology Research, 2016, v. 5, n. 2, p. 570, doi. 10.1039/c5tx00294j
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Two‐step solvothermal synthesis of high capacity LiNi<sub>0</sub><sub>.</sub><sub>8</sub>Co<sub>0</sub><sub>.</sub><sub>15</sub>Al<sub>0</sub><sub>.</sub><sub>05</sub>O<sub>2</sub> cathode for Li‐ion batteries.
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- Journal of the Chinese Chemical Society, 2021, v. 68, n. 5, p. 849, doi. 10.1002/jccs.202000429
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Preparation of hierarchical LiNi<sub>x</sub>Co<sub>y</sub>Mn<sub>z</sub>O<sub>2</sub> from solvothermal [Ni<sub>x</sub>Co<sub>y</sub>Mn<sub>z</sub>](OH)<sub>2</sub> via regulating the ratio of Ni, Co, and Mn and its excellent properties for lithium‐ion battery cathode
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- Journal of the Chinese Chemical Society, 2020, v. 67, n. 11, p. 2062, doi. 10.1002/jccs.202000051
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Hydrothermal synthesis of rod‐like CoMoO<sub>4</sub> and its excellent properties for the anode of lithium‐ion batteries.
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- Journal of the Chinese Chemical Society, 2020, v. 67, n. 11, p. 2012, doi. 10.1002/jccs.202000040
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