Abstract: Methods, systems and mediums are disclosed for aligning mass spectrometry data before the analysis of the mass spectrometry data. The mass spectrometry data may be received from a mass spectrometry machine, and re-sampled using a smooth warping function. To estimate the warping function, a synthetic signal is build using, for example, Gaussian pulses centered at a set of reference peaks. The reference peaks may be designated by users or calculated after observing a group of spectrograms. The synthetic signal is shifted and scaled so that the cross-correlation between the mass spectrometry data and the synthetic signal reaches its maximum value.

Abstract: Methods and systems are disclosed for classifying mass spectra to discriminate the absence or existence of a condition. The mass spectra may include raw mass spectrum intensity signals or may include intensity signals that have been preprocessed. The method and systems include determining a first or higher order derivative of the signals of the mass spectra, or any linear combination of the signal and a derivative of the signal, to form a mass spectra data set for training a classifier. The mass spectra data set may be provided as input to train a classifier, such as a linear discrimination classifier. The classifier trained with the derivative-based mass spectra data set then classifies mass spectra samples to improve discriminating between the absence or existence of a condition.

Abstract: Methods, systems and mediums are disclosed for aligning mass spectrometry data before the analysis of the mass spectrometry data. The mass spectrometry data may be received from a mass spectrometry machine, and re-sampled using a smooth warping function. To estimate the warping function, a synthetic signal is build using, for example, Gaussian pulses centered at a set of reference peaks. The reference peaks may be designated by users or calculated after observing a group of spectrograms. The synthetic signal is shifted and scaled so that the cross-correlation between the mass spectrometry data and the synthetic signal reaches its maximum value.

Abstract: Methods, systems and mediums are disclosed for aligning mass spectrometry data before the analysis of the mass spectrometry data. The mass spectrometry data may be received from a mass spectrometry machine, and re-sampled using a smooth warping function. To estimate the warping function, a synthetic signal is build using, for example, Gaussian pulses centered at a set of reference peaks. The reference peaks may be designated by users or calculated after observing a group of spectrograms. The synthetic signal is shifted and scaled so that the cross-correlation between the mass spectrometry data and the synthetic signal reaches its maximum value.

Abstract: Methods and systems are disclosed for classifying mass spectra to discriminate the absence or existence of a condition. The mass spectra may include raw mass spectrum intensity signals or may include intensity signals that have been preprocessed. The method and systems include determining a first or higher order derivative of the signals of the mass spectra, or any linear combination of the signal and a derivative of the signal, to form a mass spectra data set for training a classifier. The mass spectra data set is provided as input to train a classifier, such as a linear discrimination classifier. The classifier trained with the derivative-based mass spectra data set then classifies mass spectra samples to improve discriminating between the absence or existence of a condition.

Abstract: Methods and systems are disclosed for classifying mass spectra to discriminate the absence or existence of a condition. The mass spectra may include raw mass spectrum intensity signals or may include intensity signals that have been preprocessed. The method and systems include determining a first or higher order derivative of the signals of the mass spectra, or any linear combination of the signal and a derivative of the signal, to form a mass spectra data set for training a classifier. The mass spectra data set is provided as input to train a classifier, such as a linear discrimination classifier. The classifier trained with the derivative-based mass spectra data set then classifies mass spectra samples to improve discriminating between the absence or existence of a condition.