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- CalculateAutoTotal(ProteinSequence)
- ####################################################################################
A method used for computing all autocorrelation descriptors based on 8 properties of AADs.
Usage:
result=CalculateGearyAutoTotal(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30*8*3=720 normalized Moreau Broto, Moran, and Geary
autocorrelation descriptors based on the given properties(i.e., _AAPropert).
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- CalculateEachGearyAuto(ProteinSequence, AAP, AAPName)
- ####################################################################################
you can use the function to compute GearyAuto
descriptors for different properties based on AADs.
Usage:
result=CalculateEachGearyAuto(protein,AAP,AAPName)
Input: protein is a pure protein sequence.
AAP is a dict form containing the properties of 20 amino acids (e.g., _AvFlexibility).
AAPName is a string used for indicating the property (e.g., '_AvFlexibility').
Output: result is a dict form containing 30 Geary autocorrelation
descriptors based on the given property.
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- CalculateEachMoranAuto(ProteinSequence, AAP, AAPName)
- ####################################################################################
you can use the function to compute MoranAuto
descriptors for different properties based on AADs.
Usage:
result=CalculateEachMoranAuto(protein,AAP,AAPName)
Input: protein is a pure protein sequence.
AAP is a dict form containing the properties of 20 amino acids (e.g., _AvFlexibility).
AAPName is a string used for indicating the property (e.g., '_AvFlexibility').
Output: result is a dict form containing 30 Moran autocorrelation
descriptors based on the given property.
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- CalculateEachNormalizedMoreauBrotoAuto(ProteinSequence, AAP, AAPName)
- ####################################################################################
you can use the function to compute MoreauBrotoAuto
descriptors for different properties based on AADs.
Usage:
result=CalculateEachNormalizedMoreauBrotoAuto(protein,AAP,AAPName)
Input: protein is a pure protein sequence.
AAP is a dict form containing the properties of 20 amino acids (e.g., _AvFlexibility).
AAPName is a string used for indicating the property (e.g., '_AvFlexibility').
Output: result is a dict form containing 30 Normalized Moreau-Broto autocorrelation
descriptors based on the given property.
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- CalculateGearyAuto(ProteinSequence, AAProperty, AAPropertyName)
- ####################################################################################
A method used for computing GearyAuto for all properties
Usage:
result=CalculateGearyAuto(protein,AAP,AAPName)
Input: protein is a pure protein sequence.
AAProperty is a list or tuple form containing the properties of 20 amino acids (e.g., _AAProperty).
AAPName is a list or tuple form used for indicating the property (e.g., '_AAPropertyName').
Output: result is a dict form containing 30*p Geary autocorrelation
descriptors based on the given properties.
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- CalculateGearyAutoAvFlexibility(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
AvFlexibility.
Usage:
result=CalculateGearyAutoAvFlexibility(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on AvFlexibility.
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- CalculateGearyAutoFreeEnergy(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
FreeEnergy.
Usage:
result=CalculateGearyAutoFreeEnergy(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on FreeEnergy.
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- CalculateGearyAutoHydrophobicity(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
hydrophobicity.
Usage:
result=CalculateGearyAutoHydrophobicity(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on hydrophobicity.
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- CalculateGearyAutoMutability(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
Mutability.
Usage:
result=CalculateGearyAutoMutability(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on Mutability.
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- CalculateGearyAutoPolarizability(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
Polarizability.
Usage:
result=CalculateGearyAutoPolarizability(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on Polarizability.
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- CalculateGearyAutoResidueASA(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
ResidueASA.
Usage:
result=CalculateGearyAutoResidueASA(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on ResidueASA.
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- CalculateGearyAutoResidueVol(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
ResidueVol.
Usage:
result=CalculateGearyAutoResidueVol(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on ResidueVol.
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- CalculateGearyAutoSteric(ProteinSequence)
- ####################################################################################
Calculte the GearyAuto Autocorrelation descriptors based on
Steric.
Usage:
result=CalculateGearyAutoSteric(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Geary Autocorrelation
descriptors based on Steric.
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- CalculateGearyAutoTotal(ProteinSequence)
- ####################################################################################
A method used for computing Geary autocorrelation descriptors based on 8 properties of AADs.
Usage:
result=CalculateGearyAutoTotal(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30*8=240 Geary
autocorrelation descriptors based on the given properties(i.e., _AAPropert).
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- CalculateMoranAuto(ProteinSequence, AAProperty, AAPropertyName)
- ####################################################################################
A method used for computing MoranAuto for all properties
Usage:
result=CalculateMoranAuto(protein,AAP,AAPName)
Input: protein is a pure protein sequence.
AAProperty is a list or tuple form containing the properties of 20 amino acids (e.g., _AAProperty).
AAPName is a list or tuple form used for indicating the property (e.g., '_AAPropertyName').
Output: result is a dict form containing 30*p Moran autocorrelation
descriptors based on the given properties.
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- CalculateMoranAutoAvFlexibility(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
AvFlexibility.
Usage:
result=CalculateMoranAutoAvFlexibility(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on AvFlexibility.
####################################################################################
- CalculateMoranAutoFreeEnergy(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
FreeEnergy.
Usage:
result=CalculateMoranAutoFreeEnergy(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on FreeEnergy.
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- CalculateMoranAutoHydrophobicity(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on hydrophobicity.
Usage:
result=CalculateMoranAutoHydrophobicity(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on hydrophobicity.
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- CalculateMoranAutoMutability(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
Mutability.
Usage:
result=CalculateMoranAutoMutability(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on Mutability.
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- CalculateMoranAutoPolarizability(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
Polarizability.
Usage:
result=CalculateMoranAutoPolarizability(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on Polarizability.
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- CalculateMoranAutoResidueASA(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
ResidueASA.
Usage:
result=CalculateMoranAutoResidueASA(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on ResidueASA.
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- CalculateMoranAutoResidueVol(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
ResidueVol.
Usage:
result=CalculateMoranAutoResidueVol(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on ResidueVol.
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- CalculateMoranAutoSteric(ProteinSequence)
- ####################################################################################
Calculte the MoranAuto Autocorrelation descriptors based on
AutoSteric.
Usage:
result=CalculateMoranAutoSteric(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Moran Autocorrelation
descriptors based on AutoSteric.
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- CalculateMoranAutoTotal(ProteinSequence)
- ####################################################################################
A method used for computing Moran autocorrelation descriptors based on 8 properties of AADs.
Usage:
result=CalculateMoranAutoTotal(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30*8=240 Moran
autocorrelation descriptors based on the given properties(i.e., _AAPropert).
####################################################################################
- CalculateNormalizedMoreauBrotoAuto(ProteinSequence, AAProperty, AAPropertyName)
- ####################################################################################
A method used for computing MoreauBrotoAuto for all properties.
Usage:
result=CalculateNormalizedMoreauBrotoAuto(protein,AAP,AAPName)
Input: protein is a pure protein sequence.
AAProperty is a list or tuple form containing the properties of 20 amino acids (e.g., _AAProperty).
AAPName is a list or tuple form used for indicating the property (e.g., '_AAPropertyName').
Output: result is a dict form containing 30*p Normalized Moreau-Broto autocorrelation
descriptors based on the given properties.
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- CalculateNormalizedMoreauBrotoAutoAvFlexibility(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on
AvFlexibility.
Usage:
result=CalculateNormalizedMoreauBrotoAutoAvFlexibility(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on AvFlexibility.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoFreeEnergy(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on
FreeEnergy.
Usage:
result=CalculateNormalizedMoreauBrotoAutoFreeEnergy(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on FreeEnergy.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoHydrophobicity(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on
hydrophobicity.
Usage:
result=CalculateNormalizedMoreauBrotoAutoHydrophobicity(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on Hydrophobicity.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoMutability(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on Mutability.
Usage:
result=CalculateNormalizedMoreauBrotoAutoMutability(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on Mutability.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoPolarizability(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on
Polarizability.
Usage:
result=CalculateNormalizedMoreauBrotoAutoPolarizability(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on Polarizability.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoResidueASA(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on
ResidueASA.
Usage:
result=CalculateNormalizedMoreauBrotoAutoResidueASA(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on ResidueASA.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoResidueVol(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on
ResidueVol.
Usage:
result=CalculateNormalizedMoreauBrotoAutoResidueVol(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on ResidueVol.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoSteric(ProteinSequence)
- ####################################################################################
Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on Steric.
Usage:
result=CalculateNormalizedMoreauBrotoAutoSteric(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation
descriptors based on Steric.
####################################################################################
- CalculateNormalizedMoreauBrotoAutoTotal(ProteinSequence)
- ####################################################################################
A method used for computing normalized Moreau Broto autocorrelation descriptors based
on 8 proterties of AADs.
Usage:
result=CalculateNormalizedMoreauBrotoAutoTotal(protein)
Input: protein is a pure protein sequence.
Output: result is a dict form containing 30*8=240 normalized Moreau Broto
autocorrelation descriptors based on the given properties(i.e., _AAPropert).
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- NormalizeEachAAP(AAP)
- ####################################################################################
All of the amino acid indices are centralized and
standardized before the calculation.
Usage:
result=NormalizeEachAAP(AAP)
Input: AAP is a dict form containing the properties of 20 amino acids.
Output: result is the a dict form containing the normalized properties
of 20 amino acids.
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