129 related articles for article (PubMed ID: 38922065)
21. Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.
Xiao Y; Chen Y; Huang R; Jiang F; Zhou J; Yang T
BMC Med Res Methodol; 2024 Apr; 24(1):92. PubMed ID: 38643122
[TBL] [Abstract][Full Text] [Related]
22. In silico prediction of drug-induced liver injury with a complementary integration strategy based on hybrid representation.
Gu Y; Wang Y; Wu Z; Li W; Liu G; Tang Y
Mol Inform; 2023 Jul; 42(7):e2200284. PubMed ID: 37195875
[TBL] [Abstract][Full Text] [Related]
23. Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.
Feng C; Chen H; Yuan X; Sun M; Chu K; Liu H; Rui M
J Chem Inf Model; 2019 Jul; 59(7):3240-3250. PubMed ID: 31188585
[TBL] [Abstract][Full Text] [Related]
24. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.
Cui S; Luo Y; Tseng HH; Ten Haken RK; El Naqa I
Med Phys; 2019 May; 46(5):2497-2511. PubMed ID: 30891794
[TBL] [Abstract][Full Text] [Related]
25. Urine metabolomics and microbiome analyses reveal the mechanism of anti-tuberculosis drug-induced liver injury, as assessed for causality using the updated RUCAM: A prospective study.
Wang MG; Wu SQ; Zhang MM; He JQ
Front Immunol; 2022; 13():1002126. PubMed ID: 36483548
[TBL] [Abstract][Full Text] [Related]
26. Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study.
Zhong T; Zhuang Z; Dong X; Wong KH; Wong WT; Wang J; He D; Liu S
JMIR Med Inform; 2021 Jul; 9(7):e29226. PubMed ID: 34283036
[TBL] [Abstract][Full Text] [Related]
27. Predicting Drug-Induced Liver Injury with Bayesian Machine Learning.
Williams DP; Lazic SE; Foster AJ; Semenova E; Morgan P
Chem Res Toxicol; 2020 Jan; 33(1):239-248. PubMed ID: 31535850
[TBL] [Abstract][Full Text] [Related]
28. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.
Ancuceanu R; Hovanet MV; Anghel AI; Furtunescu F; Neagu M; Constantin C; Dinu M
Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32204453
[TBL] [Abstract][Full Text] [Related]
29. Development of a Battery of
Rathman J; Yang C; Ribeiro JV; Mostrag A; Thakkar S; Tong W; Hobocienski B; Sacher O; Magdziarz T; Bienfait B
Chem Res Toxicol; 2021 Feb; 34(2):601-615. PubMed ID: 33356149
[TBL] [Abstract][Full Text] [Related]
30. An ensemble learning approach for modeling the systems biology of drug-induced injury.
Aguirre-Plans J; Piñero J; Souza T; Callegaro G; Kunnen SJ; Sanz F; Fernandez-Fuentes N; Furlong LI; Guney E; Oliva B
Biol Direct; 2021 Jan; 16(1):5. PubMed ID: 33435983
[TBL] [Abstract][Full Text] [Related]
31. InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism.
Lee S; Yoo S
J Cheminform; 2024 Jan; 16(1):1. PubMed ID: 38173043
[TBL] [Abstract][Full Text] [Related]
32. Computational models for predicting liver toxicity in the deep learning era.
Mostafa F; Chen M
Front Toxicol; 2023; 5():1340860. PubMed ID: 38312894
[TBL] [Abstract][Full Text] [Related]
33. The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury.
Xi L; Yao J; Wei Y; Wu X; Yao X; Liu H; Li S
Mol Biosyst; 2017 Jan; 13(2):417-424. PubMed ID: 28092392
[TBL] [Abstract][Full Text] [Related]
34. Setting Clinical Exposure Levels of Concern for Drug-Induced Liver Injury (DILI) Using Mechanistic in vitro Assays.
Shah F; Leung L; Barton HA; Will Y; Rodrigues AD; Greene N; Aleo MD
Toxicol Sci; 2015 Oct; 147(2):500-14. PubMed ID: 26206150
[TBL] [Abstract][Full Text] [Related]
35. Identification of intrinsic hepatotoxic compounds in Polygonum multiflorum Thunb. using machine-learning methods.
Hu X; Du T; Dai S; Wei F; Chen X; Ma S
J Ethnopharmacol; 2022 Nov; 298():115620. PubMed ID: 35963419
[TBL] [Abstract][Full Text] [Related]
36. Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports.
Lesiński W; Mnich K; Rudnicki WR
Front Genet; 2021; 12():661075. PubMed ID: 34276771
[No Abstract] [Full Text] [Related]
37. Applications of In Silico Models to Predict Drug-Induced Liver Injury.
Lin J; Li M; Mak W; Shi Y; Zhu X; Tang Z; He Q; Xiang X
Toxics; 2022 Dec; 10(12):. PubMed ID: 36548621
[TBL] [Abstract][Full Text] [Related]
38. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.
Chen M; Hong H; Fang H; Kelly R; Zhou G; Borlak J; Tong W
Toxicol Sci; 2013 Nov; 136(1):242-9. PubMed ID: 23997115
[TBL] [Abstract][Full Text] [Related]
39. In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method.
Wang Y; Xiao Q; Chen P; Wang B
Int J Mol Sci; 2019 Aug; 20(17):. PubMed ID: 31443562
[TBL] [Abstract][Full Text] [Related]
40. An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning.
Li W; Liu W; Hussain Memon F; Wang B; Xu C; Dong S; Wang H; Hu Z; Quan X; Deng Y; Liu Q; Su S; Yin C
Comput Intell Neurosci; 2022; 2022():2220527. PubMed ID: 35571720
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
