A method includes exposing template polynucleotide strands, sequencing primers, and polymerase to flows of nucleotide species; obtaining a series of measured intensity values and randomly selecting a training subset therefrom; generating series of base calls using a base caller and aligning the series of base calls to a reference genome or sequence using an aligner; determining intensity value thresholds and parameters of a linear transformation corresponding to different homopolymer lengths and nucleotide species; generating series of base calls corresponding to the series of measured intensity values using at least some of the parameters of a linear transformation; and recalibrating the series of base calls corresponding to the plurality of series of measured intensity values using at least some of the intensity value thresholds.

The present invention relates to ATAC-seq data normalization for utilizing epigenetic information associated with chromatin openness, and a method for utilizing same. According to the present invention, it is possible to readily normalize and quantitatively compare ATAC-seq in various samples and various cohorts, and selected differential peaks can be used in various epigenetic studies, the diagnosis of diseases, and prediction of the prognoses of diseases.

The present invention relates to ATAC-seq data normalization for utilizing epigenetic information associated with chromatin openness, and a method for utilizing same. According to the present invention, it is possible to readily normalize and quantitatively compare ATAC-seq in various samples and various cohorts, and selected differential peaks can be used in various epigenetic studies, the diagnosis of diseases, and prediction of the prognoses of diseases.

The present invention relates to the identification of proteins involving measurement and characterisation of multidimensional aspects of said proteins.

Disclosed is a method for processing Raman data of eosinophil based on artificial intelligence, the method being executed by a device, the method including generating Raman data by performing Raman analysis using a specific wavelength on eosinophils isolated from blood of a diagnosed person, pre-processing the generated Raman data, assigning a weight to the pre-processed Raman data for each of components including a nucleus, a cell membrane, a granule, and a background, classifying data for each component based on a result of assigning the weight, extracting data in which the component is the granule based on a classified result; and determining whether a specific disease has occurred in the diagnosed person through eosinophil characteristics of the diagnosed person based on the extracted data.

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.

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.

Biomarkers are provided that are predictive of a subject's responsiveness to a therapy comprising a JAK inhibitor. The biomarkers, compositions, and methods described herein are useful in selecting appropriate treatment modalities for a subject having, suspected of having, or at risk of developing an inflammatory skin disease.

Biomarkers are provided that are predictive of a subject's responsiveness to a therapy comprising a JAK inhibitor. The biomarkers, compositions, and methods described herein are useful in selecting appropriate treatment modalities for a subject having, suspected of having, or at risk of developing an inflammatory skin disease.

Systems and methods for determining a sequence of at least a portion of a target polymer from a subject are provided. A dataset that comprises one or more image files is obtained. A combined plurality of localizations based at least in part on each respective plurality of fluorophore localizations is determined for each image file in the one or more image files. Each localization in the combined plurality of localizations includes a target polymer position identity and a spatial location. The plurality of localizations are segmented into one or more target polymer strands. Each target polymer strand corresponds to a respective subset of localizations and target polymer position identities. A respective target polymer sequence is assembled using each subset of localizations for each target polymer strand, thereby providing a set of target polymer sequences.