Article

An interpretable machine learning method for detecting novel pathogens

2020-03-17

Abstract excerpt

<title>Abstract</title> <p>Background: According to the World Health Organization (WHO), infectious diseases continue to one of the leading causes of death worldwide. Since the core microbiota flora of humans is largely diverse and horizontal gene transfer (HGT), it is very challenging to determine whether a particular bacterial strain is commensal or pathogenic to humans. With the latest advances in next-generat...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 1cbe3562-5860-55d6-b957-68585b631313
DOI: 10.21203/rs.2.20477/v2

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An interpretable machine learning method for detecting novel pathogensDOI 10.21203/rs.2.20477/v2Read

Application of mNGS in the Etiological Analysis of Lower Respiratory Tract Infections and the Prediction of Drug ResistanceSemantic proximity Read

Phenotype-Based Threat Assessment.Semantic proximity Read

PathoFact: A pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic dataSemantic proximity Read

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learningSemantic proximity Read

HyperVR–A hybrid prediction framework for virulence factors and antibiotic resistance genes in microbial dataSemantic proximity Read

PaPrBaG: A random forest approach for the detection of novel pathogens from NGS dataSemantic proximity Read

Interpretable and Accurate Prediction Models for Metagenomics DataSemantic proximity Read

Pathogenic potential prediction of <i>Vibrio parahaemolyticus</i> by using pangenome data with high performance machine learning algorithmsSemantic proximity Read

Classification of pathogenic microbes using a minimal set of single nucleotide polymorphisms derived from whole genome sequences.Semantic proximity Read

Predicting clinical outcome of<i>Escherichia coli</i>O157:H7 infections using explainable Machine LearningSemantic proximity Read

Reducing work-load of pathotype and gene detection in <i>Klebsiella pneumoniae</i> by leveraging machine learningSemantic proximity Read

Uncertainty-Aware Antimicrobial Resistance Prediction in E. coli and S. aureus Isolates Using Hybrid Bayesian Neural NetworksSemantic proximity Read