AIMC Topic: Bacteria

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Effects of data transformation and model selection on feature importance in microbiome classification data.

Microbiome
BACKGROUND: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, composi...

Gut microbial composition associated with risk of premature aging in women with Yin-deficiency constitution.

Frontiers in cellular and infection microbiology
BACKGROUND: Yin-deficiency constitution (YinDC) refers to a traditional Chinese medicine concept, characterized by an imbalance state that includes an imbalance in the gut microbiota, resulting from a relative deficiency of Yin fluids within the body...

Machine Learning-Enhanced Bacteria Detection Using a Fluorescent Sensor Array with Functionalized Graphene Quantum Dots.

ACS applied materials & interfaces
Pathogenic bacteria are the source of many serious health problems, such as foodborne diseases and hospital infections. Timely and accurate detection of these pathogens is of vital significance for disease prevention, control of epidemic spread, and ...

DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction.

Scientific reports
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Tradi...

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm.

Bioresource technology
Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predicting treated greywater quality is of great significance for water reuse. In this work, machine learning models were developed for simulating and predi...

A calibration framework toward model generalization for bacteria concentration estimation in water resource recovery facilities.

Scientific reports
Reduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it r...

Interpretation of machine learning-based prediction models and functional metagenomic approach to identify critical genes in HBCD degradation.

Journal of hazardous materials
Hexabromocyclododecane (HBCD) poses significant environmental risks, and identifying HBCD-degrading microbes and their enzymatic mechanisms is challenging due to the complexity of microbial interactions and metabolic pathways. This study aimed to ide...

Faecal metaproteomics analysis reveals a high cardiovascular risk profile across healthy individuals and heart failure patients.

Gut microbes
The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut healt...

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference.

mSphere
The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. However, it is influenced by various factors and may vary among individuals residing in distinct geographi...

Machine learning analysis of sex and menopausal differences in the gut microbiome in the HELIUS study.

NPJ biofilms and microbiomes
Sex differences in the gut microbiome have been examined previously, but results are inconsistent, often due to small sample sizes. We investigated sex and menopausal differences in the gut microbiome in a large multi-ethnic population cohort study, ...