AIMC Topic: Microbiota

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Machine learning models for pancreatic cancer diagnosis based on microbiome markers from serum extracellular vesicles.

Scientific reports
Pancreatic cancer (PC) is a fatal disease with an extremely low 5-year survival rate, mainly because of its poor detection rate in early stages. Given emerging evidence of the relationship between microbiota composition and diseases, this study aims ...

Modeling microbiome-trait associations with taxonomy-adaptive neural networks.

Microbiome
The human microbiome, a complex ecosystem of microorganisms inhabiting the body, plays a critical role in human health. Investigating its association with host traits is essential for understanding its impact on various diseases. Although shotgun met...

Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms.

Scientific reports
Microorganisms are one of the most promising biomarkers for cancer, and the relationship between microorganisms and lung cancer occurrence and development provides significant potential for pulmonary nodule (PN) diagnosis from a microbiological persp...

The dawn of the revolution that will allow us to precisely describe how microbiomes function.

Journal of proteomics
The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and others - is known as the microbiota. A holobiont, in turn, refers to an integrated ecological unit w...

NPENN: A Noise Perturbation Ensemble Neural Network for Microbiome Disease Phenotype Prediction.

IEEE journal of biomedical and health informatics
With advances in microbiomics, the crucial role of microbes in disease progression is increasingly recognized. However, predicting disease phenotypes using microbiome data remains challenging due to data complexity, heterogeneity, and limited model g...

Mediation CNN (Med-CNN) Model for High-Dimensional Mediation Data.

International journal of molecular sciences
Complex biological features such as the human microbiome and gene expressions play a crucial role in human health by mediating various biomedical processes that influence disease progression, such as immune responses and metabolic processes. Understa...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Environmental research
Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. In this study, we identified 33 different MPs features including their abundance, shape, color, size...

Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.

NPJ systems biology and applications
Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learni...

Graph-based deep learning for predictions on changes in microbiomes and biogas production in anaerobic digestion systems.

Water research
Anaerobic digestion (AD), which relies on a complex microbial consortium for efficient biogas generation, is a promising avenue for renewable energy production and organic waste treatment. However, understanding and optimising AD processes are challe...

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...