AIMC Topic: Metagenomics

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MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Journal of molecular biology
Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis te...

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.

PloS one
The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing cu...

Multimodal deep learning applied to classify healthy and disease states of human microbiome.

Scientific reports
Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes, enabling us to discover and understand microbial diseases. Compositional differences have been reported between patients and healthy people, which co...

Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.

Scientific reports
Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 104...

NGS read classification using AI.

PloS one
Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient's sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific...

Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data.

Computational and mathematical methods in medicine
The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic ...

TwinCons: Conservation score for uncovering deep sequence similarity and divergence.

PLoS computational biology
We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a 'cost' of transforming one ...

AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome.

Genes & genetic systems
In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable for big data analysis has become increasingly important. Unsupervised AI that can reveal novel know...

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data.

Journal of visualized experiments : JoVE
A variety of biological sequence classification tasks, such as species classification, gene function classification and viral host classification, are expected processes in many metagenomic data analyses. Since metagenomic data contain a large number...

MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly.

BMC bioinformatics
BACKGROUND: The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagen...