AIMC Topic: Metagenome

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Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.

IEEE/ACM transactions on computational biology and bioinformatics
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...

Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection.

IEEE/ACM transactions on computational biology and bioinformatics
To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial s...

Deep learning models for bacteria taxonomic classification of metagenomic data.

BMC bioinformatics
BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria cla...

Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

IEEE/ACM transactions on computational biology and bioinformatics
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, ...

Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.

PLoS computational biology
Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbia...

Large-scale machine learning for metagenomics sequence classification.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read i...

DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.

BMC bioinformatics
BACKGROUND: Continual progress in next-generation sequencing allows for generating increasingly large metagenomes which are over time or space. Comparing and classifying the metagenomes with different microbial communities is critical. Alignment-free...

MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning.

DNA research : an international journal for rapid publication of reports on genes and genomes
The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics ha...

DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes.

Briefings in bioinformatics
MOTIVATION: Mobile genetic elements (MGEs) play an important role in facilitating the acquisition of antibiotic resistance genes (ARGs) within microbial communities, significantly impacting the evolution of antibiotic resistance. Understanding the me...

Metagenomics-Toolkit: the flexible and efficient cloud-based metagenomics workflow featuring machine learning-enabled resource allocation.

NAR genomics and bioinformatics
The metagenome analysis of complex environments with thousands of datasets, such as those in the Sequence Read Archive, requires substantial computational resources for it to be completed within a reasonable time frame. Efficient use of infrastructur...