AIMC Topic: Sequence Analysis, Protein

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Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

Nucleic acids research Nov 18, 2019
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...
Machine Learning Animals Protein Binding Humans Protein Kinase Inhibitors Small Molecule Libraries Sequence Analysis, Protein Genes Evolution, Molecular Protein Kinases Quantitative Structure-Activity Relationship
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BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches.

Briefings in bioinformatics Jul 19, 2019
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typi...
Sequence Analysis, Protein Sequence Analysis Computational Biology Sequence Analysis, RNA Sequence Analysis, DNA Databases, Protein Internet Machine Learning Databases, Nucleic Acid Software Humans Algorithms
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NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research Jul 2, 2019
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...
Internet Benchmarking Plants Databases, Protein Structure-Activity Relationship Models, Molecular Protein Interaction Mapping Sequence Alignment Sequence Homology, Amino Acid Amino Acid Sequence Molecular Sequence Annotation Gene Ontology Humans Machine Learning Software Animals Computational Biology Proteins Sequence Analysis, Protein Prokaryotic Cells
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BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.

Bioinformatics (Oxford, England) Feb 1, 2019
MOTIVATION: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction....
Protein Domains Binding Sites Sequence Analysis, Protein Machine Learning Protein Interaction Maps Proteins
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ECO, the Evidence & Conclusion Ontology: community standard for evidence information.

Nucleic acids research Jan 8, 2019
The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product...
Humans Animals User-Computer Interface Sequence Analysis, Protein Internet Information Storage and Retrieval Computational Biology Gene Ontology Databases, Genetic Proteins
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UniProt: a worldwide hub of protein knowledge.

Nucleic acids research Jan 8, 2019
The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of the...
Proteome Databases, Protein Data Curation Sequence Analysis, Protein Knowledge Bases Molecular Sequence Annotation
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Deep learning improves antimicrobial peptide recognition.

Bioinformatics (Oxford, England) Aug 15, 2018
MOTIVATION: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laborat...
Computational Biology Anti-Infective Agents Sequence Analysis, Protein Peptides Deep Learning
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Learned protein embeddings for machine learning.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enab...
Computational Biology Proteins Models, Biological Machine Learning Software Humans Bacteria Amino Acid Sequence Eukaryota Sequence Analysis, Protein
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CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.
Vibrio cholerae Acetyltransferases Coenzyme A Bacterial Proteins Protein Conformation Sequence Analysis, Protein Support Vector Machine Proteins Models, Molecular Software Protein Binding Computational Biology
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SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification.

Bioinformatics (Oxford, England) Jul 15, 2018
SUMMARY: Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for th...
Internet Neural Networks, Computer Gene Ontology Computational Biology Proteins Sequence Analysis, Protein
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