Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312203488> ?p ?o ?g. }
- W4312203488 abstract "Abstract Background The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. Results We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. Conclusions SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies." @default.
- W4312203488 created "2023-01-04" @default.
- W4312203488 creator A5002040196 @default.
- W4312203488 creator A5003955951 @default.
- W4312203488 creator A5014133803 @default.
- W4312203488 creator A5032186893 @default.
- W4312203488 creator A5043380578 @default.
- W4312203488 creator A5061655472 @default.
- W4312203488 creator A5067913388 @default.
- W4312203488 creator A5068549081 @default.
- W4312203488 creator A5082458553 @default.
- W4312203488 creator A5084168371 @default.
- W4312203488 date "2022-12-24" @default.
- W4312203488 modified "2023-10-05" @default.
- W4312203488 title "Stochastic variational variable selection for high-dimensional microbiome data" @default.
- W4312203488 cites W1516111018 @default.
- W4312203488 cites W1968105193 @default.
- W4312203488 cites W1994616650 @default.
- W4312203488 cites W2000619947 @default.
- W4312203488 cites W2006465310 @default.
- W4312203488 cites W2025994725 @default.
- W4312203488 cites W2026065824 @default.
- W4312203488 cites W2033403400 @default.
- W4312203488 cites W2053571848 @default.
- W4312203488 cites W2053801811 @default.
- W4312203488 cites W2054765427 @default.
- W4312203488 cites W2056279562 @default.
- W4312203488 cites W2069429561 @default.
- W4312203488 cites W2072169887 @default.
- W4312203488 cites W2082630584 @default.
- W4312203488 cites W2085284704 @default.
- W4312203488 cites W2087292184 @default.
- W4312203488 cites W2099266276 @default.
- W4312203488 cites W2101998432 @default.
- W4312203488 cites W2125784987 @default.
- W4312203488 cites W2127498532 @default.
- W4312203488 cites W2133703553 @default.
- W4312203488 cites W2138556224 @default.
- W4312203488 cites W2147699265 @default.
- W4312203488 cites W2157487910 @default.
- W4312203488 cites W2165072487 @default.
- W4312203488 cites W2169012810 @default.
- W4312203488 cites W2549134075 @default.
- W4312203488 cites W2558792200 @default.
- W4312203488 cites W2749612575 @default.
- W4312203488 cites W2751236055 @default.
- W4312203488 cites W2771045365 @default.
- W4312203488 cites W2775152143 @default.
- W4312203488 cites W2800297515 @default.
- W4312203488 cites W2883705104 @default.
- W4312203488 cites W2888637453 @default.
- W4312203488 cites W2892572630 @default.
- W4312203488 cites W2910073785 @default.
- W4312203488 cites W2913042332 @default.
- W4312203488 cites W2951383526 @default.
- W4312203488 cites W2963276645 @default.
- W4312203488 cites W2970477522 @default.
- W4312203488 cites W2977433364 @default.
- W4312203488 cites W2996681417 @default.
- W4312203488 cites W3001781379 @default.
- W4312203488 cites W3010810358 @default.
- W4312203488 cites W3016697352 @default.
- W4312203488 cites W3022258762 @default.
- W4312203488 cites W3033466752 @default.
- W4312203488 cites W3035975458 @default.
- W4312203488 cites W3097807397 @default.
- W4312203488 cites W3118724893 @default.
- W4312203488 cites W3121407008 @default.
- W4312203488 cites W3123374720 @default.
- W4312203488 doi "https://doi.org/10.1186/s40168-022-01439-0" @default.
- W4312203488 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36566203" @default.
- W4312203488 hasPublicationYear "2022" @default.
- W4312203488 type Work @default.
- W4312203488 citedByCount "1" @default.
- W4312203488 countsByYear W43122034882023 @default.
- W4312203488 crossrefType "journal-article" @default.
- W4312203488 hasAuthorship W4312203488A5002040196 @default.
- W4312203488 hasAuthorship W4312203488A5003955951 @default.
- W4312203488 hasAuthorship W4312203488A5014133803 @default.
- W4312203488 hasAuthorship W4312203488A5032186893 @default.
- W4312203488 hasAuthorship W4312203488A5043380578 @default.
- W4312203488 hasAuthorship W4312203488A5061655472 @default.
- W4312203488 hasAuthorship W4312203488A5067913388 @default.
- W4312203488 hasAuthorship W4312203488A5068549081 @default.
- W4312203488 hasAuthorship W4312203488A5082458553 @default.
- W4312203488 hasAuthorship W4312203488A5084168371 @default.
- W4312203488 hasBestOaLocation W43122034881 @default.
- W4312203488 hasConcept C116834253 @default.
- W4312203488 hasConcept C124101348 @default.
- W4312203488 hasConcept C134306372 @default.
- W4312203488 hasConcept C143121216 @default.
- W4312203488 hasConcept C148483581 @default.
- W4312203488 hasConcept C154945302 @default.
- W4312203488 hasConcept C169214877 @default.
- W4312203488 hasConcept C182310444 @default.
- W4312203488 hasConcept C182365436 @default.
- W4312203488 hasConcept C18903297 @default.
- W4312203488 hasConcept C2776214188 @default.
- W4312203488 hasConcept C33923547 @default.
- W4312203488 hasConcept C41008148 @default.