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#







predict(self, sequence: AASequence) float

Returns predicted peak heights (intensities) of a single peptide


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)


predict(self, sequences: List[AASequence]) List[float]


predict(self, sequences: List[AASequence], add_info: List[List[float]]) List[float]