Patent ID: 11862298
Assignee: VERILY LIFE SCIENCES LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 18:
19. A method of training an encoder-decoder network for predicting an intensity of a fragment ion in mass spectrometry data, the method comprising:
obtaining, by a computing device, a plurality of mass spectrometry data sets comprising peptide sequences;
pre-processing, by the computing device, the plurality of mass spectrometry data sets to create a first data set and a second data set, wherein the pre-processing comprises (i) identifying spectra data with unknown peptide identities, annotating the spectra data with unknown peptide identities, and adding the spectra data with unknown peptide identities having a Q-value of a predetermined number to the first data set; and (ii) identifying spectra data with known peptide identities, annotating the spectra data with known peptide identities, and adding the spectra data with known peptide identities irrespective of their Q-value to the second data set;
inputting, by the computing device, a batch of spectra data from the first data set into the encoder-decoder network in a first training process;
adjusting, by the computing device, weights and biases of the encoder-decoder network in response to the first training process;
repeating, by the computing device, the inputting and the adjusting using other batches of spectra data from the first data set until a predetermined number of batches of spectra data from the first data set have been processed;
inputting, by the computing device, a batch of spectra data from the second data set into the encoder-decoder network in a second training process;
adjusting, by the computing device, weights and biases of the encoder-decoder network in response to the second training process;
repeating, by the computing device, the inputting and the adjusting using other batches of spectra data from the first data set until (i) the predetermined number of batches of spectra data from the first data set have been processed, or (ii) all batches of spectra data from the first data set and the second data set are processed;
determining, by the computing device, an error in successive values from the first training process and the second training process after all batches of spectra data from the first data set and the second data set are processed;
selecting, by the computing device, a validation batch of spectra data comprising spectra data from the first data set and the second data set in a 1:1 ratio;
inputting, by the computing device, the validation batch of spectra data into the encoder-decoder network in a third training process;
determining, by the computing device, an error in successive values from the first training process and the second training process after all batches of spectra data from the first training data set and the training second data set are processed;
repeating, by the computing device, the first training process, the second training process, and the third training process with different values for one or more hyperparameters;
selecting, by the computing device, an optimal set of values of the one or more hyperparameters based on error from evaluation on the first training process, the second training process, and the third training process;
selecting, by the computing device, a testing batch of spectra data comprising spectra data from the first data set and the second data set in a 1:1 ratio;
inputting, by the computing device, the testing batch of spectra data into the encoder-decoder network in a fourth training process; and
determining, by the computing device, an error on the fourth training process.