[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Chevrette et al., 2017 - Google Patents

SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria

Chevrette et al., 2017

View HTML
Document ID
3969866045913355867
Author
Chevrette M
Aicheler F
Kohlbacher O
Currie C
Medema M
Publication year
Publication venue
Bioinformatics

External Links

Snippet

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) …
Continue reading at academic.oup.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/14—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for phylogeny or evolution, e.g. evolutionarily conserved regions determination or phylogenetic tree construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Chevrette et al. SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria
Silva et al. SUPER-FOCUS: a tool for agile functional analysis of shotgun metagenomic data
Lu et al. Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics
Gurevich et al. Increased diversity of peptidic natural products revealed by modification-tolerant database search of mass spectra
Hamid et al. Identifying antimicrobial peptides using word embedding with deep recurrent neural networks
Xie et al. ISEScan: automated identification of insertion sequence elements in prokaryotic genomes
McNair et al. PHACTS, a computational approach to classifying the lifestyle of phages
Rosselló-Móra et al. Past and future species definitions for Bacteria and Archaea
Iwai et al. Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes
Medema et al. Computational approaches to natural product discovery
Bartoszewicz et al. DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks
Maetschke et al. Gene Ontology-driven inference of protein–protein interactions using inducers
Ames et al. Scalable metagenomic taxonomy classification using a reference genome database
Nesvizhskii Proteogenomics: concepts, applications and computational strategies
Sirén et al. Rapid discovery of novel prophages using biological feature engineering and machine learning
Heller et al. ARBitrator: a software pipeline for on-demand retrieval of auto-curated nifH sequences from GenBank
Singh et al. SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
Esterman et al. Evolution of DNA packaging in gene transfer agents
Bahai et al. EpitopeVec: linear epitope prediction using deep protein sequence embeddings
Tang et al. PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer
Galiez et al. VIRALpro: a tool to identify viral capsid and tail sequences
Liu et al. Phylogenetic clustering of genes reveals shared evolutionary trajectories and putative gene functions
Azad et al. Interpreting genomic data via entropic dissection
Garneau et al. High-throughput identification of viral termini and packaging mechanisms in virome datasets using PhageTermVirome
Biswas et al. ISQuest: finding insertion sequences in prokaryotic sequence fragment data