“…We employ a deep-learning-based model to identify one of the 6 Ekman’s basic emotions, or the absence of emotional expression, in comments to the Covid-19 news, and an implementation of Latent Dirichlet Allocation (LDA) to identify 12 different topics in the news messages. …”
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“…Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. …”
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“…In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. …”
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“…Participants were clustered based on their dietary intake by applying the semi-parametric Bayesian approach of the Dirichlet Process. In this approach, individuals with the same multivariate distribution based on dietary intake were assigned to the same cluster. …”
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“…The themes of the corpus were extracted using hashtags, subreddits, and Latent Dirichlet Allocation (LDA) and were analyzed using descriptive statistics. …”
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“…We established a microbiota catalog of multi-cohort athletes and found that athletes have specialized gut microbiota specific to the type of sport they engaged in. Using latent Dirichlet allocation, we identified 10 microbial subgroups of athletes’ gut microbiota, each of which had specific correlations with inflammation, diet, and anaerobic performance in different types of athletes. …”
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“…We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications. …”
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“…After the data preprocessing, Latent Dirichlet Allocation (LDA) topic modeling was conducted to identify the salient terms and topics inside the collected tweets. …”
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“…The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. …”
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“…Data sets obtained from the replicates of a single subject were used to develop a functional profile through Dirichlet mixture of beta distribution. This functional profile is flexible enough to accommodate variability of the instrument and the inherent variability of each individual, thus simultaneously addressing different sources of systematic error. …”
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“…Using a Hierarchical Dirichlet Process (HDP), we model the presence of admixture of an unknown number of ancestral populations in a given sample of genotype data. …”
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“…The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). …”
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“…Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. …”
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“…RESULTS: In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. …”
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“…First, we infer the latent topics in the sample documents using Latent Dirichlet Allocation (LDA). Next, we use logistic regression models to compute the probability that a given candidate criterion belongs to a particular topic. …”
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“…METHOD: In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. …”
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“…Partitioning of baseline communities into enterotypes by Dirichlet multinomial mixture modeling resulted in three enterotypes that corresponded with tumor burden. …”
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“…METHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of proteins, we propose higher-order LDA-obtained topic-based representations of protein structures to provide an alternative route for remote homology detection and organization of the protein structure space in few dimensions. …”
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“…Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution. …”
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“…The method, based on meta-analysis of previously-estimated QTL effects following Bennewitz and Meuwissen (J Anim Breed Genet 127:171–9, 2010), utilizes a modified Dirichlet process Gaussian mixture model (DPGMM) to fit the number of mixture components and estimate parameters (i.e. mean and variance) of the genomic distribution. …”
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