AIMC Topic: Viruses

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Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes.

Biotechnology progress
The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control su...

Virus identification in electron microscopy images by residual mixed attention network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Virus identification in electron microscopy (EM) images is considered as one of the front-line method in pathogen diagnosis and re-emerging infectious agents. However, the existing methods either focused on the detection of ...

Predicting host taxonomic information from viral genomes: A comparison of feature representations.

PLoS computational biology
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning.

BMC bioinformatics
BACKGROUND: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions ...

Machine Learning for detection of viral sequences in human metagenomic datasets.

BMC bioinformatics
BACKGROUND: Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conv...

Prediction of virus-host infectious association by supervised learning methods.

BMC bioinformatics
BACKGROUND: The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing...

Controlling testing volume for respiratory viruses using machine learning and text mining.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children wi...

Accurate contact predictions using covariation techniques and machine learning.

Proteins
Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effectiv...

Representing virus-host interactions and other multi-organism processes in the Gene Ontology.

BMC microbiology
BACKGROUND: The Gene Ontology project is a collaborative effort to provide descriptions of gene products in a consistent and computable language, and in a species-independent manner. The Gene Ontology is designed to be applicable to all organisms but...

Extracting True Virus SERS Spectra and Augmenting Data for Improved Virus Classification and Quantification.

ACS sensors
Surface-enhanced Raman spectroscopy (SERS) is a transformative tool for infectious disease diagnostics, offering rapid and sensitive species identification. However, background spectra in biological samples complicate analyte peak detection, increase...