AIMC Topic: Metagenome
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Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.
Biology direct
Dec 10, 2020
BACKGROUND: The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately wh...
A machine learning framework to determine geolocations from metagenomic profiling.
Biology direct
Nov 23, 2020
BACKGROUND: Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples' geolocations. In t...
A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study.
Biotechnology and bioengineering
Nov 14, 2020
Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are...
Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life.
BMC bioinformatics
Sep 21, 2020
BACKGROUND: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new ref...
Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.
Hypertension (Dallas, Tex. : 1979)
Sep 10, 2020
Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not cle...
Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes.
Microbiome
Feb 11, 2020
BACKGROUND: Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene t...
De novo Nanopore read quality improvement using deep learning.
BMC bioinformatics
Nov 6, 2019
BACKGROUND: Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often c...
Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.
Biology direct
Aug 20, 2019
BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacteria...
Application of machine learning techniques for creating urban microbial fingerprints.
Biology direct
Aug 16, 2019
BACKGROUND: Research has found that human associated microbial communities play a role in homeostasis and the disruption of these communities may be important in an array of medical conditions. However outside of the human body many of these communit...
Massive metagenomic data analysis using abundance-based machine learning.
Biology direct
Aug 1, 2019
BACKGROUND: Metagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms th...
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