Hybrid technique for characterization of human skin using a combined machine learning and inverse Monte Carlo approach

2019 ◽  
Author(s):  
Nina Verdel ◽  
Jovan Tanevski ◽  
Sašo Džeroski ◽  
Boris Majaron
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Human Skin ◽  
Hybrid Technique ◽  
Monte Carlo Approach ◽  
Inverse Monte Carlo

scholarly journals vRhyme enables binning of viral genomes from metagenomes

2021 ◽  
Author(s):  
Kristopher Kieft ◽  
Alyssa Adams ◽  
Rauf Salamzade ◽  
Lindsay Kalan ◽  
Karthik Anantharaman
Keyword(s):  
Machine Learning ◽  
Nitrate Reductase ◽  
Human Skin ◽  
Effect Size ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Viral Ecology ◽  
Viral Genomes ◽  
Complete Genomes

Genome binning has been essential for characterization of bacteria, archaea, and even eukaryotes from metagenomes. Yet, no approach exists for viruses. We developed vRhyme, a fast and precise software for construction of viral metagenome-assembled genomes (vMAGs). vRhyme utilizes single- or multi-sample coverage effect size comparisons between scaffolds and employs supervised machine learning to identity nucleotide feature similarities, which are compiled into iterations of weighted networks and refined bins. Using simulated viromes, we displayed superior performance of vRhyme compared to available binning tools in constructing more complete and uncontaminated vMAGs. When applied to 10,601 viral scaffolds from human skin, vRhyme advanced our understanding of resident viruses, highlighted by identification of a Herelleviridae vMAG comprised of 22 scaffolds, and another vMAG encoding a nitrate reductase metabolic gene, representing near-complete genomes post-binning. vRhyme will enable a convention of binning uncultivated viral genomes and has the potential to transform metagenome-based viral ecology.

scholarly journals Partitioning Inverse Monte Carlo Iterative Algorithm for Finding the Three Smallest Eigenpairs of Generalized Eigenvalue Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Behrouz Fathi Vajargah ◽  
Farshid Mehrdoust
Keyword(s):  
Monte Carlo ◽  
Eigenvalue Problem ◽  
Iterative Algorithm ◽  
Generalized Eigenvalue Problem ◽  
Symmetric Matrices ◽  
Monte Carlo Approach ◽  
Generalized Eigenvalue ◽  
Inverse Monte Carlo

A new Monte Carlo approach for evaluating the generalized eigenpair of real symmetric matrices will be proposed. Algorithm for the three smallest eigenpairs based on the partitioning inverse Monte Carlo iterative (IMCI) method will be considered.

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