AIMC Topic: Microbiota
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Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge.
Biology direct
Jan 4, 2021
BACKGROUND: Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the wh...
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...
Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification: A review.
Journal of microbiological methods
Sep 4, 2020
The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the env...
Learning machine approach reveals microbial signatures of diet and sex in dog.
PloS one
Aug 17, 2020
The characterization of the microbial population of many niches of the organism, as the gastrointestinal tract, is now possible thanks to the use of high-throughput DNA sequencing technique. Several studies in the companion animals field already inve...
Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.
Communications biology
Jul 15, 2020
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...
Metatranscriptomics reveals the gene functions and metabolic properties of the major microbial community during Chinese Sichuan Paocai fermentation.
Food microbiology
Jul 3, 2020
Chinese Sichuan Paocai (CSP) is one of the world's best-known fermented vegetables with a large presence in the Chinese market. The dynamic microbial community is the main contributor to Paocai fermentation. However, little is known about the ecologi...
A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.
mBio
Jun 9, 2020
Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward dev...
Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.
Critical reviews in microbiology
May 21, 2020
In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In th...
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