NFnetFu: A novel workflow for microbiome data fusion.

Bisht, Vartika, Acharjee, Animesh and Gkoutos, Georgios V (2021) NFnetFu: A novel workflow for microbiome data fusion. Computers in biology and medicine, 135. p. 104556. ISSN 1879-0534. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Official URL: http://www.journals.elsevier.com/computers-in-biol...

Abstract

Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QA Mathematics. Computing
QW Microbiology. Immunology
QZ Pathology. Oncology
WO Surgery
WU Dentistry. Oral surgery
Divisions: Ambulatory Care > Outpatients
Related URLs:
Depositing User: Jamie Edgar
Date Deposited: 14 Jul 2021 11:42
Last Modified: 14 Jul 2021 11:42
URI: http://www.repository.uhblibrary.co.uk/id/eprint/4478

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