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A sequence alignment-independent method for protein classification
John K. Vries
, Rajan Munshi
,
Dror Tobi
, Judith Klein-Seetharaman
, Panayiotis V. Benos
, Ivet Bahar
Research output
:
Contribution to journal
›
Article
›
peer-review
20
Scopus citations
Overview
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Dive into the research topics of 'A sequence alignment-independent method for protein classification'. Together they form a unique fingerprint.
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Keyphrases
Sequence Alignment
100%
Protein Classification
100%
Functional Motif
100%
Multiple Alignment
66%
Protein Family
66%
Random Chance
66%
Pfam
66%
Classification Methods
33%
Amino Acids
33%
High Probability
33%
Popular
33%
Feature Vector
33%
Classification Results
33%
Bootstrap
33%
True Positive
33%
Uniformly Distributed
33%
Jackknife Test
33%
Natural Sequences
33%
Best Matches
33%
Sequence Data
33%
Profile Hidden Markov Models (pHMMs)
33%
Single Sequence
33%
Horizontal Transfer
33%
Scoring Matrix
33%
Bayesian Framework
33%
Ordered Lists
33%
Missing Sequence
33%
Genetic Recombination
33%
Biochemistry, Genetics and Molecular Biology
Protein Family
100%
Sequence Alignment
100%
Protein Classification
100%
Amino Acids
50%
Genetic Recombination
50%
Hidden Markov Model
50%
Horizontal Transfer
50%