FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2913483642> ?p ?o ?g. }

• W2913483642 abstract "With the rapid advancement of DNA sequencing, metagenomics and metatranscriptomics have made great progress, which deepen our understanding on the human microbiome and its impact on human health and diseases. The microbiome, which is characterized by small samples, high dimensions and complicated relationships with hosts, refers to the species, genes and genomes of the microbiota, as well as the products of the microbiota and the host environment. In fact, many machine learning methods have been used to conduct Microbiome-Wide Association Studies which can link the microbiome with the phenotypes, such as the status of human health and diseases. However, existing methods such as Support Vector Machines (SVMs) have some limitations on deep representation learning with deep architectures which can promote the reuse of features and potentially lead to progressively more abstract features at higher layers of representations. Recently, Deep Neural Networks (DNNs), a kind of deep learning models, are widely used for metagenomic data analysis and can perform well on representation learning. But they are considered as a black box and sufferring from criticisms due to theirs lacking of interpretability. Thus, it is interesting to explore other deep learning models for metagenomic data analysis. In this work, we introduce a deep learning model called Deep Forest to study the microbiome associations and we also present an ensemble method for feature selection. Experimental results show that Deep Forest outperforms the traditional machine learning methods. In addition, compared to DNNs, Deep Forest has better interpretability and less hyperparameters." @default.
• W2913483642 created "2019-02-21" @default.
• W2913483642 creator A5003550654 @default.
• W2913483642 creator A5019913931 @default.
• W2913483642 creator A5025167129 @default.
• W2913483642 creator A5026817081 @default.
• W2913483642 creator A5039602194 @default.
• W2913483642 creator A5048270998 @default.
• W2913483642 creator A5087942458 @default.
• W2913483642 date "2018-12-01" @default.
• W2913483642 modified "2023-10-05" @default.
• W2913483642 title "An Ensemble Feature Selection Method Based on Deep Forest for Microbiome-Wide Association Studies" @default.
• W2913483642 cites W1501213224 @default.
• W2913483642 cites W162848086 @default.
• W2913483642 cites W1703384511 @default.
• W2913483642 cites W1964027278 @default.
• W2913483642 cites W1993784588 @default.
• W2913483642 cites W1996202672 @default.
• W2913483642 cites W2027946048 @default.
• W2913483642 cites W2028582941 @default.
• W2913483642 cites W2028963044 @default.
• W2913483642 cites W2038802458 @default.
• W2913483642 cites W2071841602 @default.
• W2913483642 cites W2072020668 @default.
• W2913483642 cites W2075333494 @default.
• W2913483642 cites W2100080086 @default.
• W2913483642 cites W2117004913 @default.
• W2913483642 cites W2133856765 @default.
• W2913483642 cites W2143426320 @default.
• W2913483642 cites W2152751713 @default.
• W2913483642 cites W2157490046 @default.
• W2913483642 cites W2165250079 @default.
• W2913483642 cites W2395461901 @default.
• W2913483642 cites W2413207665 @default.
• W2913483642 cites W2463312000 @default.
• W2913483642 cites W2469986620 @default.
• W2913483642 cites W2473355215 @default.
• W2913483642 cites W2502949459 @default.
• W2913483642 cites W2528898077 @default.
• W2913483642 cites W2530623388 @default.
• W2913483642 cites W2592340788 @default.
• W2913483642 cites W2609405928 @default.
• W2913483642 cites W2611882427 @default.
• W2913483642 cites W2726093206 @default.
• W2913483642 cites W2795896985 @default.
• W2913483642 cites W2963776453 @default.
• W2913483642 cites W4205699531 @default.
• W2913483642 doi "https://doi.org/10.1109/bibm.2018.8621461" @default.
• W2913483642 hasPublicationYear "2018" @default.
• W2913483642 type Work @default.
• W2913483642 sameAs 2913483642 @default.
• W2913483642 citedByCount "8" @default.
• W2913483642 countsByYear W29134836422019 @default.
• W2913483642 countsByYear W29134836422021 @default.
• W2913483642 countsByYear W29134836422022 @default.
• W2913483642 crossrefType "proceedings-article" @default.
• W2913483642 hasAuthorship W2913483642A5003550654 @default.
• W2913483642 hasAuthorship W2913483642A5019913931 @default.
• W2913483642 hasAuthorship W2913483642A5025167129 @default.
• W2913483642 hasAuthorship W2913483642A5026817081 @default.
• W2913483642 hasAuthorship W2913483642A5039602194 @default.
• W2913483642 hasAuthorship W2913483642A5048270998 @default.
• W2913483642 hasAuthorship W2913483642A5087942458 @default.
• W2913483642 hasConcept C104317684 @default.
• W2913483642 hasConcept C108583219 @default.
• W2913483642 hasConcept C119857082 @default.
• W2913483642 hasConcept C12267149 @default.
• W2913483642 hasConcept C143121216 @default.
• W2913483642 hasConcept C15151743 @default.
• W2913483642 hasConcept C154945302 @default.
• W2913483642 hasConcept C169258074 @default.
• W2913483642 hasConcept C2522767166 @default.
• W2913483642 hasConcept C2781067378 @default.
• W2913483642 hasConcept C41008148 @default.
• W2913483642 hasConcept C45942800 @default.
• W2913483642 hasConcept C55493867 @default.
• W2913483642 hasConcept C60644358 @default.
• W2913483642 hasConcept C70721500 @default.
• W2913483642 hasConcept C86803240 @default.
• W2913483642 hasConcept C91478284 @default.
• W2913483642 hasConceptScore W2913483642C104317684 @default.
• W2913483642 hasConceptScore W2913483642C108583219 @default.
• W2913483642 hasConceptScore W2913483642C119857082 @default.
• W2913483642 hasConceptScore W2913483642C12267149 @default.
• W2913483642 hasConceptScore W2913483642C143121216 @default.
• W2913483642 hasConceptScore W2913483642C15151743 @default.
• W2913483642 hasConceptScore W2913483642C154945302 @default.
• W2913483642 hasConceptScore W2913483642C169258074 @default.
• W2913483642 hasConceptScore W2913483642C2522767166 @default.
• W2913483642 hasConceptScore W2913483642C2781067378 @default.
• W2913483642 hasConceptScore W2913483642C41008148 @default.
• W2913483642 hasConceptScore W2913483642C45942800 @default.
• W2913483642 hasConceptScore W2913483642C55493867 @default.
• W2913483642 hasConceptScore W2913483642C60644358 @default.
• W2913483642 hasConceptScore W2913483642C70721500 @default.
• W2913483642 hasConceptScore W2913483642C86803240 @default.
• W2913483642 hasConceptScore W2913483642C91478284 @default.
• W2913483642 hasLocation W29134836421 @default.
• W2913483642 hasOpenAccess W2913483642 @default.
• W2913483642 hasPrimaryLocation W29134836421 @default.

• next