371 related articles for article (PubMed ID: 31420049)
1. 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]
2. 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]
3. Massive metagenomic data analysis using abundance-based machine learning.
Harris ZN; Dhungel E; Mosior M; Ahn TH
Biol Direct; 2019 Aug; 14(1):12. PubMed ID: 31370905
[TBL] [Abstract][Full Text] [Related]
4. A machine learning framework to determine geolocations from metagenomic profiling.
Huang L; Xu C; Yang W; Yu R
Biol Direct; 2020 Nov; 15(1):27. PubMed ID: 33225966
[TBL] [Abstract][Full Text] [Related]
5. Environmental metagenome classification for constructing a microbiome fingerprint.
Kawulok J; Kawulok M; Deorowicz S
Biol Direct; 2019 Nov; 14(1):20. PubMed ID: 31722729
[TBL] [Abstract][Full Text] [Related]
6. Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge.
Zhang R; Walker AR; Datta S
Biol Direct; 2021 Jan; 16(1):1. PubMed ID: 33397406
[TBL] [Abstract][Full Text] [Related]
7. Unraveling bacterial fingerprints of city subways from microbiome 16S gene profiles.
Walker AR; Grimes TL; Datta S; Datta S
Biol Direct; 2018 May; 13(1):10. PubMed ID: 29789016
[TBL] [Abstract][Full Text] [Related]
8. Profiling microbial strains in urban environments using metagenomic sequencing data.
Zolfo M; Asnicar F; Manghi P; Pasolli E; Tett A; Segata N
Biol Direct; 2018 May; 13(1):9. PubMed ID: 29743119
[TBL] [Abstract][Full Text] [Related]
9. Supervised Machine Learning Enables Geospatial Microbial Provenance.
Bhattacharya C; Tierney BT; Ryon KA; Bhattacharyya M; Hastings JJA; Basu S; Bhattacharya B; Bagchi D; Mukherjee S; Wang L; Henaff EM; Mason CE
Genes (Basel); 2022 Oct; 13(10):. PubMed ID: 36292799
[TBL] [Abstract][Full Text] [Related]
10. The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report.
MetaSUB International Consortium
Microbiome; 2016 Jun; 4(1):24. PubMed ID: 27255532
[TBL] [Abstract][Full Text] [Related]
11. Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.
Chen JC; Tyler AD
Biol Direct; 2020 Dec; 15(1):29. PubMed ID: 33302990
[TBL] [Abstract][Full Text] [Related]
12. Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.
Casimiro-Soriguer CS; Loucera C; Perez Florido J; López-López D; Dopazo J
Biol Direct; 2019 Aug; 14(1):15. PubMed ID: 31429791
[TBL] [Abstract][Full Text] [Related]
13. Unraveling City-Specific Microbial Signatures and Identifying Sample Origins for the Data From CAMDA 2020 Metagenomic Geolocation Challenge.
Zhang R; Ellis D; Walker AR; Datta S
Front Genet; 2021; 12():659650. PubMed ID: 34421984
[TBL] [Abstract][Full Text] [Related]
14. Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association.
Wu J; Danko D; Afshinnekoo E; Bezdan D; Bhattacharyya M; Castro-Nallar E; Chmielarczyk A; Hazrin-Chong NH; Deng Y; Dias-Neto E; Frolova A; Mason-Buck G; Iraola G; Jang S; Łabaj P; Lee PKH; Nieto-Caballero M; Osuolale OO; Ouzounis CA; Perlin MH; Prithiviraj B; Rascovan N; Różańska A; Schriml LM; Semmler T; Suzuki H; Ugalde JA; Young B; Werner J; Zambrano MM; Zhao Y; Mason C; Shi T;
Environ Res; 2022 May; 207():112183. PubMed ID: 34637759
[TBL] [Abstract][Full Text] [Related]
15. MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.
Qiao Y; Jia B; Hu Z; Sun C; Xiang Y; Wei C
Biol Direct; 2018 Aug; 13(1):15. PubMed ID: 30134953
[TBL] [Abstract][Full Text] [Related]
16. Fingerprinting cities: differentiating subway microbiome functionality.
Zhu C; Miller M; Lusskin N; Mahlich Y; Wang Y; Zeng Z; Bromberg Y
Biol Direct; 2019 Oct; 14(1):19. PubMed ID: 31666099
[TBL] [Abstract][Full Text] [Related]
17. Assessment of urban microbiome assemblies with the help of targeted in silico gold standards.
Gerner SM; Rattei T; Graf AB
Biol Direct; 2018 Oct; 13(1):22. PubMed ID: 30621760
[TBL] [Abstract][Full Text] [Related]
18. Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data.
Zhelyazkova M; Yordanova R; Mihaylov I; Kirov S; Tsonev S; Danko D; Mason C; Vassilev D
Front Genet; 2021; 12():642991. PubMed ID: 33763122
[TBL] [Abstract][Full Text] [Related]
19. Where environmental microbiome meets its host: Subway and passenger microbiome relationships.
Peimbert M; Alcaraz LD
Mol Ecol; 2023 May; 32(10):2602-2618. PubMed ID: 35318755
[TBL] [Abstract][Full Text] [Related]
20. Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes.
Olson ND; Treangen TJ; Hill CM; Cepeda-Espinoza V; Ghurye J; Koren S; Pop M
Brief Bioinform; 2019 Jul; 20(4):1140-1150. PubMed ID: 28968737
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
