Abstract: The application discloses in vitro methods for diagnosing lung cancer in a subject, wherein the method comprises detecting at least one biomarker selected from the group consisting of Rho GDP dissociation inhibitor beta (ARHGDIB), alpha-tubulin 4A (TUBA4A), glutathione S-transferase omega 1 (GSTO1), filamin A (FLNA), peroxiredoxin 6 (PRDX6) and cadherin 13 (CDH13) in a biological sample from the subject, and kits for measuring said at least one biomarker.

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: The present invention provides a method for monitoring esophageal adenocarcinoma (EAC) disease progression in a subject, the method comprising: obtaining a biological sample from the subject; measuring with a quantitative analytical method at least one metabolite; determining a metabolomic biosignature of EAC disease progression based on a comparison of quantitative data for the at least one metabolite to corresponding data obtained for at least one reference sample; and identifying active EAC disease progression in the subject if the quantity of the at least one metabolite in the sample from the subject is greater than that found in the at least one reference sample.

Abstract: Methods are provided to detect and treat a fungal infection. The method may include the steps of obtaining a sample from a subject suspected of having a fungal infection, detecting an Uncharacterized Fungal Protein (CIMG_09001/CPSG_01366) in the sample, and determining the presence on the fungal infection if the Uncharacterized Fungal Protein is detected.

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 detect and treat a fungal infection. The method may include the steps of obtaining a sample from a subject suspected of having a fungal infection, detecting an Uncharacterized Fungal Protein (CIMG_09001/CPSG_01366) in the sample, and determining the presence on the fungal infection if the Uncharacterized Fungal Protein is detected.