AIMC Topic: Metagenomics
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An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.
BMC bioinformatics
Sep 18, 2023
BACKGROUND: The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed t...
ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.
PLoS computational biology
May 1, 2023
The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging...
EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction Through Metagenomics.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 3, 2023
A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimension...
Unlocking the microbial studies through computational approaches: how far have we reached?
Environmental science and pollution research international
Mar 15, 2023
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking...
The need for an integrated multi-OMICs approach in microbiome science in the food system.
Comprehensive reviews in food science and food safety
Jan 12, 2023
Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing dema...
RNN-VirSeeker: A Deep Learning Method for Identification of Short Viral Sequences From Metagenomes.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 3, 2022
Viruses are the most abundant biological entities on earth, and play vital roles in many aspects of microbial communities. As major human pathogens, viruses have caused huge mortality and morbidity to human society in history. Metagenomic sequencing ...
MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.
Journal of molecular biology
Apr 18, 2022
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
Apr 15, 2022
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
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...
Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.
Scientific reports
Jan 10, 2022
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...
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