AIMC Topic: Metagenomics

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Machine Learning for detection of viral sequences in human metagenomic datasets.

BMC bioinformatics Sep 24, 2018
BACKGROUND: Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conv...
Base Sequence Viruses Machine Learning Algorithms ROC Curve Sequence Analysis, DNA Metagenomics Computational Biology Databases, Genetic Humans Neural Networks, Computer
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HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders.

Genetics in medicine : official journal of the American College of Medical Genetics Sep 10, 2018
Recent dramatic advances in multiomics research coupled with exponentially increasing volume, complexity, and interdisciplinary nature of publications are making it challenging for scientists to stay up-to-date on the literature. Strategies to addres...
Databases, Factual Research Pharmacogenetics Databases, Genetic Humans Genomics Knowledge Bases Metabolomics Computational Biology Metagenomics Proteomics
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Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring.

Molecular ecology resources Aug 3, 2018
Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations ...
Bacteria Environmental Monitoring Supervised Machine Learning Biodiversity Biomarkers Eukaryota Computer Simulation Metagenomics DNA Barcoding, Taxonomic RNA, Ribosomal
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Deep learning models for bacteria taxonomic classification of metagenomic data.

BMC bioinformatics Jul 9, 2018
BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria cla...
RNA, Ribosomal, 16S Databases, Genetic Metagenomics Time Factors Bacteria Models, Genetic Metagenome Neural Networks, Computer Algorithms Reproducibility of Results Deep Learning
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Tracking antibiotic resistance gene pollution from different sources using machine-learning classification.

Microbiome May 24, 2018
BACKGROUND: Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non...
Sewage Metagenomics Soil Microbiology Drug Resistance, Microbial High-Throughput Nucleotide Sequencing China Bacteria Gastrointestinal Microbiome Anti-Bacterial Agents Animals Humans Machine Learning
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Phylogenetic convolutional neural networks in metagenomics.

BMC bioinformatics Mar 8, 2018
BACKGROUND: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architectu...
Humans Neural Networks, Computer Databases, Genetic Principal Component Analysis Metagenomics Reproducibility of Results Phylogeny Data Analysis Support Vector Machine Algorithms Inflammatory Bowel Diseases
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Gene Prediction in Metagenomic Fragments with Deep Learning.

BioMed research international Nov 8, 2017
Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagen...
Genes, Bacterial Sequence Analysis, DNA Machine Learning Models, Statistical Bacteria Metagenomics Databases, Genetic
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Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

IEEE/ACM transactions on computational biology and bioinformatics Oct 4, 2017
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, ...
Machine Learning Phenotype Cluster Analysis Metagenomics Metagenome Humans
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SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare?

BMC genomics Mar 14, 2017
BACKGROUND: A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to...
Archaea Algorithms Microbiota Phylogeny Bacteria Metagenomics RNA, Ribosomal, 16S Gene Ontology Databases, Genetic
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Prediction of virus-host infectious association by supervised learning methods.

BMC bioinformatics Mar 14, 2017
BACKGROUND: The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing...
Reproducibility of Results ROC Curve Likelihood Functions Metagenomics Viruses Support Vector Machine Bacteria Sequence Analysis, DNA Host-Pathogen Interactions DNA, Viral Logistic Models Bayes Theorem Genome, Viral Models, Theoretical
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