PeakIntensityPredictor

class pyopenms.PeakIntensityPredictor

Bases: object

Cython implementation of _PeakIntensityPredictor

Documentation is available at http://www.openms.de/current_doxygen/html/classOpenMS_1_1PeakIntensityPredictor.html

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__()

Cython signature: void PeakIntensityPredictor()

Methods

__init__	Cython signature: void PeakIntensityPredictor()
predict	Cython signature: double predict(AASequence & sequence)

predict()

• Cython signature: double predict(AASequence & sequence) Returns predicted peak heights (intensities) of a single peptide

• Cython signature: double predict(AASequence & sequence, libcpp_vector[double] & add_info)

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)

• Cython signature: libcpp_vector[double] predict(libcpp_vector[AASequence] & sequences)

• Cython signature: libcpp_vector[double] predict(libcpp_vector[AASequence] & sequences, libcpp_vector[libcpp_vector[double]] & add_info)