297 related articles for article (PubMed ID: 32108316)
1. Machine learning methods for microbiome studies.
Namkung J
J Microbiol; 2020 Mar; 58(3):206-216. PubMed ID: 32108316
[TBL] [Abstract][Full Text] [Related]
2. Taxonomy-aware feature engineering for microbiome classification.
Oudah M; Henschel A
BMC Bioinformatics; 2018 Jun; 19(1):227. PubMed ID: 29907097
[TBL] [Abstract][Full Text] [Related]
3. Identification of city specific important bacterial signature for the MetaSUB CAMDA challenge microbiome data.
Walker AR; Datta S
Biol Direct; 2019 Jul; 14(1):11. PubMed ID: 31340852
[TBL] [Abstract][Full Text] [Related]
4. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.
McAllister P; Zheng H; Bond R; Moorhead A
Comput Biol Med; 2018 Apr; 95():217-233. PubMed ID: 29549733
[TBL] [Abstract][Full Text] [Related]
5. Predicting postmortem interval based on microbial community sequences and machine learning algorithms.
Liu R; Gu Y; Shen M; Li H; Zhang K; Wang Q; Wei X; Zhang H; Wu D; Yu K; Cai W; Wang G; Zhang S; Sun Q; Huang P; Wang Z
Environ Microbiol; 2020 Jun; 22(6):2273-2291. PubMed ID: 32227435
[TBL] [Abstract][Full Text] [Related]
6. Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.
Thompson J; Johansen R; Dunbar J; Munsky B
PLoS One; 2019; 14(7):e0215502. PubMed ID: 31260460
[TBL] [Abstract][Full Text] [Related]
7. Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease.
Giuffrè M; Moretti R; Tiribelli C
Int J Mol Sci; 2023 Mar; 24(6):. PubMed ID: 36982303
[TBL] [Abstract][Full Text] [Related]
8. Application of machine learning techniques for creating urban microbial fingerprints.
Ryan FJ
Biol Direct; 2019 Aug; 14(1):13. PubMed ID: 31420049
[TBL] [Abstract][Full Text] [Related]
9. Profiling of the Conjunctival Bacterial Microbiota Reveals the Feasibility of Utilizing a Microbiome-Based Machine Learning Model to Differentially Diagnose Microbial Keratitis and the Core Components of the Conjunctival Bacterial Interaction Network.
Ren Z; Li W; Liu Q; Dong Y; Huang Y
Front Cell Infect Microbiol; 2022; 12():860370. PubMed ID: 35558101
[TBL] [Abstract][Full Text] [Related]
10. A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.
Kalluçi E; Preni B; Dhamo X; Noka E; Bardhi S; Macchia A; Bonetti G; Dhuli K; Donato K; Bertelli M; Zambrano LJM; Janaqi S
Clin Ter; 2024; 175(3):98-116. PubMed ID: 38767067
[TBL] [Abstract][Full Text] [Related]
11. Machine learning-based approaches for cancer prediction using microbiome data.
Freitas P; Silva F; Sousa JV; Ferreira RM; Figueiredo C; Pereira T; Oliveira HP
Sci Rep; 2023 Jul; 13(1):11821. PubMed ID: 37479864
[TBL] [Abstract][Full Text] [Related]
12. Targeted sequencing of clade-specific markers from skin microbiomes for forensic human identification.
Schmedes SE; Woerner AE; Novroski NMM; Wendt FR; King JL; Stephens KM; Budowle B
Forensic Sci Int Genet; 2018 Jan; 32():50-61. PubMed ID: 29065388
[TBL] [Abstract][Full Text] [Related]
13. MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics.
Maringanti VS; Bucci V; Gerber GK
mSystems; 2022 Oct; 7(5):e0013222. PubMed ID: 36069455
[TBL] [Abstract][Full Text] [Related]
14. MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks.
Lo C; Marculescu R
BMC Bioinformatics; 2019 Jun; 20(Suppl 12):314. PubMed ID: 31216991
[TBL] [Abstract][Full Text] [Related]
15. Machine Learning Methods in Computational Toxicology.
Baskin II
Methods Mol Biol; 2018; 1800():119-139. PubMed ID: 29934890
[TBL] [Abstract][Full Text] [Related]
16. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.
Mendez KM; Reinke SN; Broadhurst DI
Metabolomics; 2019 Nov; 15(12):150. PubMed ID: 31728648
[TBL] [Abstract][Full Text] [Related]
17. Robust biomarker discovery for microbiome-wide association studies.
Zhu Q; Li B; He T; Li G; Jiang X
Methods; 2020 Feb; 173():44-51. PubMed ID: 31238097
[TBL] [Abstract][Full Text] [Related]
18. Population Informative Markers Selected Using Wright's Fixation Index and Machine Learning Improves Human Identification Using the Skin Microbiome.
Sherier AJ; Woerner AE; Budowle B
Appl Environ Microbiol; 2021 Sep; 87(20):e0120821. PubMed ID: 34379455
[TBL] [Abstract][Full Text] [Related]
19. Supervised machine learning-based classification of oral malodor based on the microbiota in saliva samples.
Nakano Y; Takeshita T; Kamio N; Shiota S; Shibata Y; Suzuki N; Yoneda M; Hirofuji T; Yamashita Y
Artif Intell Med; 2014 Feb; 60(2):97-101. PubMed ID: 24439218
[TBL] [Abstract][Full Text] [Related]
20. Machine learning on microbiome research in gastrointestinal cancer.
Cheung H; Yu J
J Gastroenterol Hepatol; 2021 Apr; 36(4):817-822. PubMed ID: 33880761
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]