162 related articles for article (PubMed ID: 32545393)
1. The Role of Structural Representation in the Performance of a Deep Neural Network for X-Ray Spectroscopy.
Madkhali MMM; Rankine CD; Penfold TJ
Molecules; 2020 Jun; 25(11):. PubMed ID: 32545393
[TBL] [Abstract][Full Text] [Related]
2. A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra.
Rankine CD; Madkhali MMM; Penfold TJ
J Phys Chem A; 2020 May; 124(21):4263-4270. PubMed ID: 32369378
[TBL] [Abstract][Full Text] [Related]
3. Beyond structural insight: a deep neural network for the prediction of Pt L
Watson L; Rankine CD; Penfold TJ
Phys Chem Chem Phys; 2022 Apr; 24(16):9156-9167. PubMed ID: 35393987
[TBL] [Abstract][Full Text] [Related]
4. Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments.
Madkhali MMM; Rankine CD; Penfold TJ
Phys Chem Chem Phys; 2021 Apr; 23(15):9259-9269. PubMed ID: 33885072
[TBL] [Abstract][Full Text] [Related]
5. Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network.
Rankine CD; Penfold TJ
J Chem Phys; 2022 Apr; 156(16):164102. PubMed ID: 35490005
[TBL] [Abstract][Full Text] [Related]
6. Predicting drug-target interaction network using deep learning model.
You J; McLeod RD; Hu P
Comput Biol Chem; 2019 Jun; 80():90-101. PubMed ID: 30939415
[TBL] [Abstract][Full Text] [Related]
7. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.
Kim SG; Theera-Ampornpunt N; Fang CH; Harwani M; Grama A; Chaterji S
BMC Syst Biol; 2016 Aug; 10 Suppl 2(Suppl 2):54. PubMed ID: 27490187
[TBL] [Abstract][Full Text] [Related]
8. Multi-edge X-ray absorption spectroscopy. 1. X-ray absorption near-edge structure analysis of a biomimetic model of FeFe-hydrogenase.
Giles LJ; Grigoropoulos A; Szilagyi RK
J Phys Chem A; 2012 Dec; 116(50):12280-98. PubMed ID: 23145835
[TBL] [Abstract][Full Text] [Related]
9. Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy.
Marcella N; Liu Y; Timoshenko J; Guan E; Luneau M; Shirman T; Plonka AM; van der Hoeven JES; Aizenberg J; Friend CM; Frenkel AI
Phys Chem Chem Phys; 2020 Sep; 22(34):18902-18910. PubMed ID: 32393945
[TBL] [Abstract][Full Text] [Related]
10. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.
Xuan P; Sun H; Wang X; Zhang T; Pan S
Int J Mol Sci; 2019 Jul; 20(15):. PubMed ID: 31349729
[TBL] [Abstract][Full Text] [Related]
11. Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning.
Liu Y; Marcella N; Timoshenko J; Halder A; Yang B; Kolipaka L; Pellin MJ; Seifert S; Vajda S; Liu P; Frenkel AI
J Chem Phys; 2019 Oct; 151(16):164201. PubMed ID: 31675887
[TBL] [Abstract][Full Text] [Related]
12. DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network.
Shi Q; Chen W; Huang S; Jin F; Dong Y; Wang Y; Xue Z
Bioinformatics; 2019 Dec; 35(24):5128-5136. PubMed ID: 31197306
[TBL] [Abstract][Full Text] [Related]
13. Light-induced relaxation of photolyzed carbonmonoxy myoglobin: a temperature-dependent x-ray absorption near-edge structure (XANES) study.
Arcovito A; Lamb DC; Nienhaus GU; Hazemann JL; Benfatto M; Della Longa S
Biophys J; 2005 Apr; 88(4):2954-64. PubMed ID: 15681649
[TBL] [Abstract][Full Text] [Related]
14. Structural investigation of lanthanoid coordination: a combined XANES and molecular dynamics study.
D'Angelo P; Zitolo A; Migliorati V; Mancini G; Persson I; Chillemi G
Inorg Chem; 2009 Nov; 48(21):10239-48. PubMed ID: 19788258
[TBL] [Abstract][Full Text] [Related]
15. Latent Representation Learning for Structural Characterization of Catalysts.
Routh PK; Liu Y; Marcella N; Kozinsky B; Frenkel AI
J Phys Chem Lett; 2021 Mar; 12(8):2086-2094. PubMed ID: 33620230
[TBL] [Abstract][Full Text] [Related]
16. Enabling data-limited chemical bioactivity predictions through deep neural network transfer learning.
Liu R; Laxminarayan S; Reifman J; Wallqvist A
J Comput Aided Mol Des; 2022 Dec; 36(12):867-878. PubMed ID: 36272041
[TBL] [Abstract][Full Text] [Related]
17. Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.
Liu R; Madore M; Glover KP; Feasel MG; Wallqvist A
Toxicol Sci; 2018 Aug; 164(2):512-526. PubMed ID: 29722883
[TBL] [Abstract][Full Text] [Related]
18. Machine learning approaches for ELNES/XANES.
Mizoguchi T; Kiyohara S
Microscopy (Oxf); 2020 Apr; 69(2):92-109. PubMed ID: 31993623
[TBL] [Abstract][Full Text] [Related]
19. Uncertainty quantification of spectral predictions using deep neural networks.
Verma S; Aznan NKN; Garside K; Penfold TJ
Chem Commun (Camb); 2023 Jun; 59(46):7100-7103. PubMed ID: 37218454
[TBL] [Abstract][Full Text] [Related]
20. X-ray absorption spectroscopy of hemes and hemeproteins in solution: multiple scattering analysis.
D'Angelo P; Lapi A; Migliorati V; Arcovito A; Benfatto M; Roscioni OM; Meyer-Klaucke W; Della-Longa S
Inorg Chem; 2008 Nov; 47(21):9905-18. PubMed ID: 18837548
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
