PeakIntensityPredictor#
- class pyopenms.PeakIntensityPredictor#
Bases:
object
Cython implementation of _PeakIntensityPredictor
Original C++ documentation is available here
Predict peak heights of peptides based on Local Linear Map model
This class can be used for predictions of peptide peak heights (referred to as intensities) from a peptide sequence by a Local Linear Map (LLM) model A general introduction to the Peak Intensity Predictor (PIP) can be found in the PIP Tutorial
The predictor performs only on the peptides sequences as an AASequence representation. Every sequence is transformed to an 18 dimensional data vector representing certain chemical characteristics and is loaded into the trained LocalLinearMap model to find the predicted peptides peak intensity
Every predictor object calls the appropriate LocalLinearMap model, transforms the given sequences and creates a vector space in which the LocalLinearMap performs
- __init__(self) None #
Methods
__init__
(self)Overload:
- predict()#
Overload:
- predict(self, sequence: AASequence) float
Returns predicted peak heights (intensities) of a single peptide
Overload:
- predict(self, sequence: AASequence, add_info: List[float]) float
Returns predicted peak heights (intensities) of a single peptide
Some additional information for each peptide is returned in add_info For each peptide a row with the following components is returned: - 0: x coordinates of associated cluster (first column) - 1: y coordinates of associated cluster (2nd column) - 2: error (RMSE) of the peptide to the associated next prototype (cluster center)
Overload:
- predict(self, sequences: List[AASequence]) List[float]
Overload:
- predict(self, sequences: List[AASequence], add_info: List[List[float]]) List[float]