Feature-based multiple models improve classification of mutation-induced stability changes

BMC Genomics

Volumn 15, Issue , 2014, Pages

  Folkman, Lukas ^a,b   Stantic, Bela ^a   Sattar, Abdul ^a,b  

^a GRIFFITH UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; EVOLUTIONARY AMINO ACID AND STRUCTURAL ENCODINGS WITH ACCESSIBLE SURFACE AREA; EVOLUTIONARY AMINO ACID AND STRUCTURAL ENCODINGS WITH AMINO ACID PARAMETERS; EVOLUTIONARY AMINO ACID AND STRUCTURAL ENCODINGS WITH MULTIPLE MODELS; EVOLUTIONARY AMINO ACID AND STRUCTURAL ENCODINGS WITH SECONDARY STRUCTURE; FALSE POSITIVE RESULT; GENE MUTATION; INTERMETHOD COMPARISON; MACHINE LEARNING; MEASUREMENT ACCURACY; PREDICTIVE VALUE; PROTEIN SECONDARY STRUCTURE; PROTEIN STABILITY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; ARTIFICIAL INTELLIGENCE; CHEMICAL STRUCTURE; GENETICS; METABOLISM; MUTATION; SUPPORT VECTOR MACHINE;

PROTEIN;

ARTIFICIAL INTELLIGENCE; MODELS, MOLECULAR; MUTATION; PROTEINS; ROC CURVE; SUPPORT VECTOR MACHINES;

EID: 84925287376     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/1471-2164-15-S4-S6     Document Type: Article

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