190 related articles for article (PubMed ID: 34999303)
1. Deep learning for retention time prediction in reversed-phase liquid chromatography.
Fedorova ES; Matyushin DD; Plyushchenko IV; Stavrianidi AN; Buryak AK
J Chromatogr A; 2022 Feb; 1664():462792. PubMed ID: 34999303
[TBL] [Abstract][Full Text] [Related]
2. Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules.
Ju R; Liu X; Zheng F; Lu X; Xu G; Lin X
Anal Chem; 2021 Nov; 93(47):15651-15658. PubMed ID: 34780148
[TBL] [Abstract][Full Text] [Related]
3. Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry.
Randazzo GM; Bileck A; Danani A; Vogt B; Groessl M
J Chromatogr A; 2020 Feb; 1612():460661. PubMed ID: 31708215
[TBL] [Abstract][Full Text] [Related]
4. The METLIN small molecule dataset for machine learning-based retention time prediction.
Domingo-Almenara X; Guijas C; Billings E; Montenegro-Burke JR; Uritboonthai W; Aisporna AE; Chen E; Benton HP; Siuzdak G
Nat Commun; 2019 Dec; 10(1):5811. PubMed ID: 31862874
[TBL] [Abstract][Full Text] [Related]
5. Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification.
Yang Q; Ji H; Lu H; Zhang Z
Anal Chem; 2021 Feb; 93(4):2200-2206. PubMed ID: 33406817
[TBL] [Abstract][Full Text] [Related]
6. Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning.
Yang Q; Ji H; Fan X; Zhang Z; Lu H
J Chromatogr A; 2021 Oct; 1656():462536. PubMed ID: 34563892
[TBL] [Abstract][Full Text] [Related]
7. Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data.
Kensert A; Bouwmeester R; Efthymiadis K; Van Broeck P; Desmet G; Cabooter D
Anal Chem; 2021 Nov; 93(47):15633-15641. PubMed ID: 34780168
[TBL] [Abstract][Full Text] [Related]
8. Deep graph convolutional network for small-molecule retention time prediction.
Kang Q; Fang P; Zhang S; Qiu H; Lan Z
J Chromatogr A; 2023 Nov; 1711():464439. PubMed ID: 37865024
[TBL] [Abstract][Full Text] [Related]
9. A deep convolutional neural network for the estimation of gas chromatographic retention indices.
Matyushin DD; Sholokhova AY; Buryak AK
J Chromatogr A; 2019 Dec; 1607():460395. PubMed ID: 31405570
[TBL] [Abstract][Full Text] [Related]
10. Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.
Bonini P; Kind T; Tsugawa H; Barupal DK; Fiehn O
Anal Chem; 2020 Jun; 92(11):7515-7522. PubMed ID: 32390414
[TBL] [Abstract][Full Text] [Related]
11. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.
Hirohara M; Saito Y; Koda Y; Sato K; Sakakibara Y
BMC Bioinformatics; 2018 Dec; 19(Suppl 19):526. PubMed ID: 30598075
[TBL] [Abstract][Full Text] [Related]
12. Probabilistic metabolite annotation using retention time prediction and meta-learned projections.
García CA; Gil-de-la-Fuente A; Barbas C; Otero A
J Cheminform; 2022 Jun; 14(1):33. PubMed ID: 35672784
[TBL] [Abstract][Full Text] [Related]
13. Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction.
Morid MA; Sheng ORL; Kawamoto K; Abdelrahman S
J Biomed Inform; 2020 Nov; 111():103565. PubMed ID: 32980530
[TBL] [Abstract][Full Text] [Related]
14. Deep Learning Based Metabolite Annotation.
Chau HYK; Ao H; Zhang X; Gao S; Varghese RS; Ressom HW
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38082953
[TBL] [Abstract][Full Text] [Related]
15. RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.
Xue J; Wang B; Ji H; Li W
Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38402516
[TBL] [Abstract][Full Text] [Related]
16. Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction.
Hentabli H; Bengherbia B; Saeed F; Salim N; Nafea I; Toubal A; Nasser M
Int J Mol Sci; 2022 Oct; 23(21):. PubMed ID: 36362018
[TBL] [Abstract][Full Text] [Related]
17. Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets.
Parinet J
Chemosphere; 2021 Jul; 275():130036. PubMed ID: 33676277
[TBL] [Abstract][Full Text] [Related]
18. Predicting Kováts Retention Indices Using Graph Neural Networks.
Qu C; Schneider BI; Kearsley AJ; Keyrouz W; Allison TC
J Chromatogr A; 2021 Jun; 1646():462100. PubMed ID: 33892256
[TBL] [Abstract][Full Text] [Related]
19. DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning.
Wang S; Liu J; Ding M; Gao Y; Liu D; Tian Q; Zhu J
Comb Chem High Throughput Screen; 2022; 25(4):642-650. PubMed ID: 33605851
[TBL] [Abstract][Full Text] [Related]
20. Deep neural networks for human microRNA precursor detection.
Zheng X; Fu X; Wang K; Wang M
BMC Bioinformatics; 2020 Jan; 21(1):17. PubMed ID: 31931701
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
