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Multiclass Disease Classification from Microbial Whole-Community Metagenomes

Saad Khan, Libusha Kelly
2020 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Here, we utilize 5643 aggregated, annotated whole-community metagenomes to implement the first multiclass microbiome disease classifier of this scale, able to discriminate between 18 different diseases  ...  The microbiome, the community of microorganisms living within an individual, is a promising avenue for developing non-invasive methods for disease screening and diagnosis.  ...  Conclusion We have extended the results of previous work on microbiome-phenotype prediction here by demonstrating that multiclass disease prediction from whole community metagenomes, a clinically relevant  ... 
pmid:31797586 pmcid:PMC7120658 fatcat:nvmoyhcn7vgxfbdb25pwohuyva

Multiclass Disease Classification from Microbial Whole-Community Metagenomes using Graph Convolutional Neural Networks [article]

Saad Khan, Libusha Kelly
2019 bioRxiv   pre-print
Here, we utilize 5643 aggregated, annotated whole-community metagenomes from 19 different diseases to implement the first multiclass microbiome disease classifier of this scale.  ...  Together, these results indicate that there are predictive, disease specific signatures across microbiomes which could potentially be used for diagnostic purposes.  ...  disease prediction from whole community metagenomes, a clinically relevant task for machine learning, is a tractable problem and is improved by using the taxonomic structure of bacterial communities.  ... 
doi:10.1101/726901 fatcat:esslikyydve4hinucjipchzng4

Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data

Zhenqiu Liu, Dechang Chen, Li Sheng, Amy Y. Liu, Mikael Boden
2013 PLoS ONE  
penalties for binary and multiclass classifications with metagenomic count data (metalinprog). We evaluated the performance of our method on several real and simulation datasets.  ...  It is important to develop novel statistical learning tools for the prediction of associations between bacterial communities and disease phenotypes and for the detection of differentially abundant features  ...  The ultimate goal, however, is to identify specific microbia and microbial communities that are associated with human diseases.  ... 
doi:10.1371/journal.pone.0053253 pmid:23555553 pmcid:PMC3608598 fatcat:ustthvgj3neutkwhnhoskrtpcm

Phylogeny-based classification of microbial communities

Olga Tanaseichuk, James Borneman, Tao Jiang
2013 Computer applications in the biosciences : CABIOS  
in microbial community data encoded by a phylogenetic tree.  ...  Results: We proposed a novel supervised classification method for microbial community samples, where each sample is represented as a set of OTU frequencies, which takes advantage of the natural structure  ...  The whole genome shotgun sequencing (i.e. metagenomics) approach can also be used to study microbial community composition.  ... 
doi:10.1093/bioinformatics/btt700 pmid:24369151 fatcat:lcvht6or3jgz7pvrutekehzjum

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data

Zhenqiu Liu, William Hsiao, Brandi L. Cantarel, Elliott Franco Drábek, Claire Fraser-Liggett
2011 Computer applications in the biosciences : CABIOS  
While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of  ...  instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from  ...  Whole genome shotgun (WGS) sequencing of the community (a metagenome), on the other hand, can provide estimates of functional capabilities of microbiome (Turnbaugh et al., 2007) , but the cost is substantially  ... 
doi:10.1093/bioinformatics/btr547 pmid:21984758 pmcid:PMC3223360 fatcat:hdaefkn6kvhzpihjx2nioppleq

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

Laura Judith Marcos-Zambrano, Kanita Karaduzovic-Hadziabdic, Tatjana Loncar Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland, Aleksandra Gruca, Jasminka Hasic, Karel Hron, Thomas Klammsteiner, Mikhail Kolev (+17 others)
2021 Frontiers in Microbiology  
to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures.  ...  Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse  ...  biomarker discovery in metagenomic data (16S rRNA gene and whole-genome shotgun datasets).  ... 
doi:10.3389/fmicb.2021.634511 pmid:33737920 pmcid:PMC7962872 fatcat:wbun4lkwwjen5ccdy4zb7mnz3q

Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier

Samuel Anyaso-Samuel, Archie Sachdeva, Subharup Guha, Somnath Datta
2021 Frontiers in Genetics  
Our results highlight the unreliability of restricting the classification of metagenomic samples to source origins to a single classification algorithm.  ...  Microbiome samples harvested from urban environments can be informative in predicting the geographic location of unknown samples.  ...  Data from metagenomic sequencing technologies empower accurate estimation of the abundance of microbial communities in samples from different locations and environments.  ... 
doi:10.3389/fgene.2021.642282 pmid:33959149 pmcid:PMC8093763 fatcat:ic4bhflzerb67gqbkkl3kkccre

The Presence of Periodontal Pathogens in Gastric Cancer [article]

Marcel A. de Leeuw, Manuel X. Duval
2020 bioRxiv   pre-print
addition to Helicobacter pylori, but are not in perfect alignment, possibly due to variable parameters in the experiments, including downstream processing.MethodsHere, we analysed gastric mucosa samples from  ...  Gastric mucosa community types We applied unsupervised clustering to investigate microbial gastric mucosa community structure, irrespective of sample disease status.  ...  On the other hand, report has been made that H. pylori presence did not affect microbial community composition [Bik et al. 2006 ].  ... 
doi:10.1101/2020.03.23.003426 fatcat:xrgnctcwr5bgtfqpwopmoov2we

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Baiba Vilne, Irēna Meistere, Lelde Grantiņa-Ieviņa, Juris Ķibilds
2019 Frontiers in Microbiology  
Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)  ...  Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable  ...  Moreover, most food samples are complex, harboring composite microbial communities.  ... 
doi:10.3389/fmicb.2019.01722 pmid:31447800 pmcid:PMC6691741 fatcat:3kuz7hgocrepjemyb5gb7mg7my

Measuring the microbiome: best practices for developing and benchmarking microbiomics methods

Nicholas A. Bokulich, Michal Ziemski, Michael Robeson, Benjamin Kaehler
2020 Computational and Structural Biotechnology Journal  
datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data.  ...  Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial  ...  The functional potential of a microbial community can be inferred indirectly by marker-gene surveys [47] [48] [49] [50] , or through direct observation of the functional genes and pathways by whole-metagenome  ... 
doi:10.1016/j.csbj.2020.11.049 pmid:33363701 pmcid:PMC7744638 fatcat:yxf7jkhepzelfgwwnls6d7e2du

Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission

Michelle G Rooks, Patrick Veiga, Leslie H Wardwell-Scott, Timothy Tickle, Nicola Segata, Monia Michaud, Carey Ann Gallini, Chloé Beal, Johan ET van Hylckama-Vlieg, Sonia A Ballal, Xochitl C Morgan, Jonathan N Glickman (+3 others)
2014 The ISME Journal  
For details on Illumina shotgun sequencing, sequence processing and microbial gene and pathway Whole metagenome shotgun (WMS) sequence analysis RT-qPCR for FMP strains For detailed RT-qPCR (real-time  ...  operational taxonomic unit (OTU) selection, microbial composition and community structure analyses, metagenome inference and metabolic pathway reconstruction, and microbial biomarker discovery, see Supplementary  ... 
doi:10.1038/ismej.2014.3 pmid:24500617 pmcid:PMC4069400 fatcat:zld5ezwq3nfrdb726gpbfxq77i

Supervised classification of human microbiota

Dan Knights, Elizabeth K. Costello, Rob Knight
2011 FEMS Microbiology Reviews  
Recent advances in DNA sequencing technology have allowed the collection of high-dimensional data from human-associated microbial communities on an unprecedented scale.  ...  To encourage the development of new approaches to supervised classification of microbiota, we discuss several structures inherent in microbial community data that may be available for exploitation in novel  ...  Binary classifiers can still be made to perform multiclass classification by collecting votes from one-vs.-one (pairwise) or one-vs.  ... 
doi:10.1111/j.1574-6976.2010.00251.x pmid:21039646 fatcat:xogtw3uwzbe7jnn47htbky2yum

A Content-Based Retrieval Framework for Whole Metagenome Sequencing Samples

Duygu Dede Şener, Daniele Santoni, Giovanni Felici, Hasan Oğul
2018 Journal of Integrative Bioinformatics  
The framework consists of feature extraction, selection methods and similarity measures for whole metagenome sequencing samples. Performance of the developed framework was evaluated on given samples.  ...  Over the recent years, content-based retrieval has been suggested by various studies from different perspectives.  ...  Introduction Metagenomes, so-called random community genomes, are used to study microbial communities from various habitats [1] .  ... 
doi:10.1515/jib-2017-0067 pmid:30367805 pmcid:PMC6348744 fatcat:v2dk2wplergvpivxtw5jy2d4k4


Cristina Solé, Susie Guilly, Kevin Da Silva, Marta Llopis, Emmanuelle Le-Chatelier, Patricia Huelin, Marta Carol, Rebeca Moreira, Núria Fabrellas, Gloria De Prada, Laura Napoleone, Isabel Graupera (+11 others)
2020 Gastroenterology  
Microbial genes were grouped into clusters, denoted as metagenomic species(MGS). Cirrhosis was associated with a remarkable reduction in gene and MGS richness compared to healthy subjects.  ...  To investigate the gut microbiome in patients with cirrhosis encompassing the whole spectrum of disease: compensated, acutely decompensated without ACLF, and ACLF.  ...  This author discloses the following: Pere Ginès has participated on Advisory Boards for Novartis, Grifols, Promethera, Sequana, Intercept, and Martin Pharmaceuticals and has received research support from  ... 
doi:10.1053/j.gastro.2020.08.054 pmid:32941879 fatcat:qhcfotfzrbhivhfrevgxhbgybq

From Genomes to Phenotypes: Traitar, the Microbial Trait Analyzer

Aaron Weimann, Kyra Mooren, Jeremy Frank, Phillip B. Pope, Andreas Bremges, Alice C. McHardy, Nicola Segata
2016 mSystems  
We describe Traitar, the microbial trait analyzer, which is a fully automated software package for deriving phenotypes from a genome sequence.  ...  Traits are often used to characterize and distinguish bacteria and are likely a driving factor in microbial community composition, yet little is known about the traits of most microbes.  ...  in microbial community composition (2) .  ... 
doi:10.1128/msystems.00101-16 pmid:28066816 pmcid:PMC5192078 fatcat:gsrewjcypjhfdpx476zlxyklly
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