AIMC Topic: Genome, Bacterial

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Explainability in transformer models for functional genomics.

Briefings in bioinformatics Sep 2, 2021
The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs fr...
DNA, Bacterial Transcription Factors Escherichia coli Deep Learning Genomics Binding Sites Genome, Bacterial Transcription Initiation Site Base Sequence Promoter Regions, Genetic
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CyanoPATH: a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms.

Briefings in bioinformatics Jul 20, 2021
CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters. Based on the literature of empirical studies and genome/protein databases, it summarize...
Cyanobacteria Harmful Algal Bloom Knowledge Bases Databases, Genetic 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...
Deep Learning Bacteria Virulence Factors Genome, Bacterial Databases, Protein Bacterial Proteins
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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...
Machine Learning Software Genome, Archaeal Clustered Regularly Interspaced Short Palindromic Repeats Genome, Bacterial
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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 ...
Serogroup Genome, Bacterial Phylogeny Cattle Whole Genome Sequencing Shiga-Toxigenic Escherichia coli Escherichia coli Infections Machine Learning Animals Humans
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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,...
Open Reading Frames Machine Learning Proteins Genome, Bacterial Algorithms
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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...
Animals Swine Genome-Wide Association Study Salmonella Infections, Animal Cattle Birds Humans Salmonella Infections Machine Learning Salmonella typhimurium Genome, Bacterial Whole Genome Sequencing Host Specificity
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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...
Alphaproteobacteria Prophages Genes, Bacterial Support Vector Machine Genome, Bacterial Genes, Viral
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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-...
Neural Networks, Computer Genome, Bacterial Bacterial Proteins Software Gram-Negative Bacteria Algorithms
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Tracing outbreaks with machine learning.

Nature reviews. Microbiology May 1, 2019
Salmonella typhimurium Foodborne Diseases Public Health Disease Outbreaks Genome, Bacterial Salmonella Food Poisoning Humans Machine Learning
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