AIMC Topic: Genome, Microbial

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Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method.

mSystems Mar 7, 2023
Comprehensive protein function annotation is essential for understanding microbiome-related disease mechanisms in the host organisms. However, a large portion of human gut microbial proteins lack functional annotation. Here, we have developed a new m...
Genome, Microbial Microbiota Deep Learning Metagenome Humans Molecular Sequence Annotation
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Multimodal deep learning applied to classify healthy and disease states of human microbiome.

Scientific reports Jan 17, 2022
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...
Microbiota Liver Cirrhosis Metagenomics Healthy Volunteers Deep Learning Diabetes Mellitus, Type 2 Colorectal Neoplasms Genome, Microbial Metagenome Humans Inflammatory Bowel Diseases
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A biochemically-interpretable machine learning classifier for microbial GWAS.

Nature communications May 22, 2020
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machi...
Isoniazid Reproducibility of Results Genome, Bacterial Genome, Microbial Anti-Bacterial Agents Pyrazinamide Machine Learning Mycobacterium tuberculosis Aminosalicylic Acid Genome-Wide Association Study Drug Resistance, Bacterial
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GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.

Briefings in bioinformatics May 1, 2025
Structural variants (SVs) in microbial genomes play a critical role in phenotypic changes, environmental adaptation, and species evolution, with deletion variations particularly closely linked to phenotypic traits. Therefore, accurate and comprehensi...
Neural Networks, Computer Genome, Microbial Genomic Structural Variation Computational Biology Algorithms
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Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.

Nucleic acids research Apr 10, 2025
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...
Genome, Microbial Secondary Metabolism Biosynthetic Pathways Multigene Family Metagenome Neural Networks, Computer Genome, Bacterial Phylogeny
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PanKB: An interactive microbial pangenome knowledgebase for research, biotechnological innovation, and knowledge mining.

Nucleic acids research Jan 6, 2025
The exponential growth of microbial genome data presents unprecedented opportunities for unlocking the potential of microorganisms. The burgeoning field of pangenomics offers a framework for extracting insights from this big biological data. Recent a...
Genomics Databases, Genetic Biotechnology Genome, Bacterial Data Mining Knowledge Bases Genome, Microbial
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DeepCheck: multitask learning aids in assessing microbial genome quality.

Briefings in bioinformatics Sep 23, 2024
Metagenomic analyses facilitate the exploration of the microbial world, advancing our understanding of microbial roles in ecological and biological processes. A pivotal aspect of metagenomic analysis involves assessing the quality of metagenome-assem...
Genome, Microbial Deep Learning Metagenome Metagenomics Machine Learning Software
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