Abstract: Methods are provided to classify and identify features in mass spectral data using neural network algorithms. A convolutional neural network (CNN) was trained to identify amino acids from an unknown protein sample. The CNN was trained using known peptide sequences to predict amino acid presence, diversity, and frequency, peptide length, subsequences of amino acids classified by features include aliphatic/aromatic, hydrophobic/hydrophilic, positive/negative charge, and combinations thereof. Mass spectra data of a sample unknown to the trained CNN was discretized into a one-dimensional vector and input into the CNN. The CNN models can potentially be integrated to determine the complete peptide sequence from a spectrum, thereby improving the yield of identifiable protein sequences from mass spec analysis.

Abstract: Methods are provided to classify and identify features in mass spectral data using neural network algorithms. A convolutional neural network (CNN) was trained to identify amino acids from an unknown protein sample. The CNN was trained using known peptide sequences to predict amino acid presence, diversity, and frequency, peptide length, subsequences of amino acids classified by features include aliphatic/aromatic, hydrophobic/hydrophilic, positive/negative charge, and combinations thereof. Mass spectra data of a sample unknown to the trained CNN was discretized into a one-dimensional vector and input into the CNN. The CNN models can potentially be integrated to determine the complete peptide sequence from a spectrum, thereby improving the yield of identifiable protein sequences from mass spec analysis.

Abstract: Methods are provided to classify and identify features in mass spectral data using neural network algorithms. A convolutional neural network (CNN) was trained to identify amino acids from an unknown protein sample. The CNN was trained using known peptide sequences to predict amino acid presence, diversity, and frequency, peptide length, subsequences of amino acids classified by features include aliphatic/aromatic, hydrophobic/hydrophilic, positive/negative charge, and combinations thereof. Mass spectra data of a sample unknown to the trained CNN was discretized into a one-dimensional vector and input into the CNN. The CNN models can potentially be integrated to determine the complete peptide sequence from a spectrum, thereby improving the yield of identifiable protein sequences from mass spec analysis.

Abstract: A circuit measures the signal propagation delay through a selected test circuit. The test circuit is provided with a feedback path so that the test circuit and feedback path together form a free-running oscillator. The oscillator then automatically provides its own test signal that includes alternating rising and falling signal transitions on the test-circuit input node. A phase discriminator samples the output of the oscillator and accumulates data representing the signal propagation delay of either rising or falling signal transitions propagating through the test circuit. The worst-case delay associated with the test circuit can then be expressed as the longer of the two. Knowing the precise worst-case delay allows IC A designers to minimize the guard band and consequently guarantee higher speed performance.

Abstract: A circuit measures the signal propagation delay through a selected test circuit. The test circuit is provided with a feedback path so that the test circuit and feedback path together form a free-running oscillator. The oscillator then automatically provides its own test signal that includes alternating rising and falling signal transitions on the test-circuit input node. A phase discriminator samples the output of the oscillator and accumulates data representing the signal propagation delay of either rising or falling signal transitions propagating through the test circuit. The worst-case delay associated with the test circuit can then be expressed as the longer of the two. Knowing the precise worst-case delay allows IC designers to minimize the guard band and consequently guarantee higher speed performance.