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Microbiome datasets are often comprised of different representations or views which provide complementary information to understand microbial communities, such as metabolic pathways, taxonomic assignments, and gene families. Data integration methods including approaches based on nonnegative matrix factorization (NMF) combine multi-view data to create a comprehensive view of a given microbiome study...
Many datasets existed in the real world are often comprised of different representations or views which provide complementary information to each other. For example, microbiome datasets can be represented by metabolic paths, taxonomic assignment or gene families. To integrate information from multiple views, data integration approaches such as methods based on nonnegative matrix factorization (NMF)...
In recent years, there are growing interests in developing novel approaches for inferring dynamic interactions in biological systems including gene transcription network and microbial interaction networks. Multivariate Vector Autoregression (MVAR) model is one of these efficient methods. Variants of MVAR with different penalties or regularizations can avoid the problem of over-fitting and provide...
Visualization of large-scale data is the first step to acquire preliminary insight into complex biological data. In recent years, many statistical visualization methods have been designed to support data visualization. Stochastic Neighbor Embedding (SNE) is one of these efficient approaches, which uses the probabilistic distance to model differences among data points within the data space. SNE and...
Current data mining and statistical methods to extract patterns and relationships in microbiomic data are often based on several assumptions such as Euclidean, linear, continuous and metric space which may not be the true space of microbiomic data. For example, the microbial profiles (functional and taxonomic classifications) are often correlated in a hierarchical style. These assumptions prevent...
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