AIMC Topic: Genome, Bacterial
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DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.
Briefings in bioinformatics
May 20, 2021
Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational met...
CRISPRidentify: identification of CRISPR arrays using machine learning approach.
Nucleic acids research
Feb 26, 2021
CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are p...
Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2021
Escherichia coli is a species of bacteria that can be present in a wide variety of mammalian hosts and potentially soil environments. E. coli has an open genome and can show considerable diversity in gene content between isolates. It is a reasonable ...
OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.
Database : the journal of biological databases and curation
Jan 1, 2020
Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However,...
A guide to machine learning for bacterial host attribution using genome sequence data.
Microbial genomics
Dec 1, 2019
With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generated from both diagnostic and research laboratories for the foreseeable future, then there is both an o...
Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents.
Genome biology and evolution
Oct 1, 2019
Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some b...
DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence.
Bioinformatics (Oxford, England)
Jun 1, 2019
MOTIVATION: Various bacterial pathogens can deliver their secreted substrates also called effectors through Type III secretion systems (T3SSs) into host cells and cause diseases. Since T3SS secreted effectors (T3SEs) play important roles in pathogen-...
Tracing outbreaks with machine learning.
Nature reviews. Microbiology
May 1, 2019
Victors: a web-based knowledge base of virulence factors in human and animal pathogens.
Nucleic acids research
Jan 8, 2019
Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (ht...
A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains.
Bioinformatics (Oxford, England)
Jul 1, 2018
MOTIVATION: Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or susceptible to certain antibiotics is essential in fighting against antibiotic-resistant pathogens....
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