156 related articles for article (PubMed ID: 31666099)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
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. 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]
8. 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]
9. Passenger-surface microbiome interactions in the subway of Mexico City.
Vargas-Robles D; Gonzalez-Cedillo C; Hernandez AM; Alcaraz LD; Peimbert M
PLoS One; 2020; 15(8):e0237272. PubMed ID: 32813719
[TBL] [Abstract][Full Text] [Related]
10. Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes.
Ma ZS
BMC Bioinformatics; 2024 Mar; 25(1):137. PubMed ID: 38553666
[TBL] [Abstract][Full Text] [Related]
11. Temporal, compositional, and functional differences in the microbiome of Bangkok subway air environment.
Siriarchawatana P; Pumkaeo P; Harnpicharnchai P; Likhitrattanapisal S; Mayteeworakoon S; Boonsin W; Zhou X; Liang J; Cai L; Ingsriswang S
Environ Res; 2023 Feb; 219():115065. PubMed ID: 36535389
[TBL] [Abstract][Full Text] [Related]
12. Co-occurrence patterns of bacteria within microbiome of Moscow subway.
Klimenko NS; Tyakht AV; Toshchakov SV; Shevchenko MA; Korzhenkov AA; Afshinnekoo E; Mason CE; Alexeev DG
Comput Struct Biotechnol J; 2020; 18():314-322. PubMed ID: 32071708
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15.
Luglio DG; Katsigeorgis M; Hess J; Kim R; Adragna J; Raja A; Gordon C; Fine J; Thurston G; Gordon T; Vilcassim MJR
Environ Health Perspect; 2021 Feb; 129(2):27001. PubMed ID: 33565894
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. The single-species metagenome: subtyping
Joseph SJ; Li B; Petit Iii RA; Qin ZS; Darrow L; Read TD
PeerJ; 2016; 4():e2571. PubMed ID: 27781166
[TBL] [Abstract][Full Text] [Related]
19. Can the New Subway Line Openings Mitigate PM10 Concentration? Evidence from Chinese Cities Based on the PSM-DID Method.
Wang Y; Tao J; Wang R; Mi C
Int J Environ Res Public Health; 2020 Jun; 17(13):. PubMed ID: 32605137
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]