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ASAP: A machine learning framework for local protein properties
Nadav Brandes
, Dan Ofer
,
Michal Linial
*
*
Corresponding author for this work
Department of Biological Chemistry
Research output
:
Contribution to journal
›
Article
›
peer-review
18
Scopus citations
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Dive into the research topics of 'ASAP: A machine learning framework for local protein properties'. Together they form a unique fingerprint.
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Keyphrases
Amino Acid Sequence
100%
Annotation Prediction
100%
Protein Properties
100%
Machine Learning Framework
100%
Sequence Annotation
100%
Post-translational Modification
37%
Residue Level
37%
Cleavage Site
25%
Mass Spectrometry
12%
Secondary Structure
12%
Proteome
12%
Computational Methods
12%
Protein Sequence
12%
Machine Learning Techniques
12%
Bioactive Peptides
12%
Sequence Prediction
12%
High Performance
12%
Protein Design
12%
Protein Function
12%
False Positive Rate
12%
Web Application
12%
Search Engine
12%
Prediction Method
12%
Intrinsically Disordered
12%
Disorder Profile
12%
Convertase
12%
Rule-based
12%
Protein Precursors
12%
Protein Cleavage
12%
Heterogeneous Properties
12%
Traditional Rule
12%
Ratemaking
12%
Minimal Sequence
12%
Universal Machine
12%
Unknown Protein
12%
Machine Learning Classifiers
12%
Phosphoserine
12%
Easy Integration
12%
External Features
12%
Solvent Accessibility
12%
Rule-based Method
12%
PSSM Profile
12%
Prediction Function
12%
Python API
12%
Substantial Similarity
12%
Biochemistry, Genetics and Molecular Biology
Peptide Sequence
100%
Protein Property
100%
Posttranslational Modification
33%
Protein Secondary Structure
11%
Precursor
11%
Proteome
11%
Mass Spectrometry
11%
Protein Precursor
11%
Protein Design
11%
Protein Function
11%
Pythonidae
11%
Protein Cleavage
11%
Convertase
11%
Search Engine
11%
Phosphoserine
11%
Genomics
11%
Bioactive Peptide
11%