142 related articles for article (PubMed ID: 38329145)
1. In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.
Zhou Y; Wang Z; Huang Z; Li W; Chen Y; Yu X; Tang Y; Liu G
J Appl Toxicol; 2024 Jun; 44(6):892-907. PubMed ID: 38329145
[TBL] [Abstract][Full Text] [Related]
2.
Kang Y; Jeong B; Lim DH; Lee D; Lim KM
J Toxicol Environ Health A; 2021 Dec; 84(23):960-972. PubMed ID: 34328061
[TBL] [Abstract][Full Text] [Related]
3. Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.
Ciallella HL; Russo DP; Aleksunes LM; Grimm FA; Zhu H
Lab Invest; 2021 Apr; 101(4):490-502. PubMed ID: 32778734
[TBL] [Abstract][Full Text] [Related]
4. XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity.
Jaganathan K; Rehman MU; Tayara H; Chong KT
Int J Mol Sci; 2022 Dec; 23(24):. PubMed ID: 36555297
[TBL] [Abstract][Full Text] [Related]
5. Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.
Long TZ; Shi SH; Liu S; Lu AP; Liu ZQ; Li M; Hou TJ; Cao DS
J Chem Inf Model; 2023 Jan; 63(1):111-125. PubMed ID: 36472475
[TBL] [Abstract][Full Text] [Related]
6. In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.
Hua Y; Shi Y; Cui X; Li X
Mol Divers; 2021 Aug; 25(3):1585-1596. PubMed ID: 34196933
[TBL] [Abstract][Full Text] [Related]
7. An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.
Jaganathan K; Tayara H; Chong KT
Pharmaceutics; 2022 Apr; 14(4):. PubMed ID: 35456666
[TBL] [Abstract][Full Text] [Related]
8. Review of machine learning and deep learning models for toxicity prediction.
Guo W; Liu J; Dong F; Song M; Li Z; Khan MKH; Patterson TA; Hong H
Exp Biol Med (Maywood); 2023 Nov; 248(21):1952-1973. PubMed ID: 38057999
[TBL] [Abstract][Full Text] [Related]
9. In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches.
Xu M; Yang H; Liu G; Tang Y; Li W
J Appl Toxicol; 2022 Nov; 42(11):1766-1776. PubMed ID: 35653511
[TBL] [Abstract][Full Text] [Related]
10. Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.
Moukheiber L; Mangione W; Moukheiber M; Maleki S; Falls Z; Gao M; Samudrala R
Molecules; 2022 May; 27(9):. PubMed ID: 35566372
[TBL] [Abstract][Full Text] [Related]
11. A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.
Saravanan KM; Wan JF; Dai L; Zhang J; Zhang JZH; Zhang H
Methods; 2024 Jun; 226():164-175. PubMed ID: 38702021
[TBL] [Abstract][Full Text] [Related]
12. In silico prediction of drug-induced developmental toxicity by using machine learning approaches.
Zhang H; Mao J; Qi HZ; Ding L
Mol Divers; 2020 Nov; 24(4):1281-1290. PubMed ID: 31486961
[TBL] [Abstract][Full Text] [Related]
13. Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.
Zou J; Shen YK; Wu SN; Wei H; Li QJ; Xu SH; Ling Q; Kang M; Liu ZL; Huang H; Chen X; Wang YX; Liao XL; Tan G; Shao Y
Technol Cancer Res Treat; 2024; 23():15330338231219352. PubMed ID: 38233736
[No Abstract] [Full Text] [Related]
14. Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity.
Seo M; Choi J; Park J; Yu WJ; Kim S
Chemosphere; 2024 Feb; 349():140926. PubMed ID: 38092168
[TBL] [Abstract][Full Text] [Related]
15. In Silico Prediction of Chemical-Induced Hepatocellular Hypertrophy Using Molecular Descriptors.
Ambe K; Ishihara K; Ochibe T; Ohya K; Tamura S; Inoue K; Yoshida M; Tohkin M
Toxicol Sci; 2018 Apr; 162(2):667-675. PubMed ID: 29309657
[TBL] [Abstract][Full Text] [Related]
16. In silico prediction of hERG blockers using machine learning and deep learning approaches.
Chen Y; Yu X; Li W; Tang Y; Liu G
J Appl Toxicol; 2023 Oct; 43(10):1462-1475. PubMed ID: 37093028
[TBL] [Abstract][Full Text] [Related]
17. The applications of deep learning algorithms on in silico druggable proteins identification.
Yu L; Xue L; Liu F; Li Y; Jing R; Luo J
J Adv Res; 2022 Nov; 41():219-231. PubMed ID: 36328750
[TBL] [Abstract][Full Text] [Related]
18. A review on machine learning methods for in silico toxicity prediction.
Idakwo G; Luttrell J; Chen M; Hong H; Zhou Z; Gong P; Zhang C
J Environ Sci Health C Environ Carcinog Ecotoxicol Rev; 2018; 36(4):169-191. PubMed ID: 30628866
[TBL] [Abstract][Full Text] [Related]
19. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.
Rifaioglu AS; Atas H; Martin MJ; Cetin-Atalay R; Atalay V; Doğan T
Brief Bioinform; 2019 Sep; 20(5):1878-1912. PubMed ID: 30084866
[TBL] [Abstract][Full Text] [Related]
20. Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.
Feng H; Zhang L; Li S; Liu L; Yang T; Yang P; Zhao J; Arkin IT; Liu H
Toxicol Lett; 2021 Apr; 340():4-14. PubMed ID: 33421549
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
