Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

In accordance with some embodiments, systems, methods, and media for automatically predicting a classification of incidental adrenal tumors based on clinical variables and urinary steroid levels are provided. In some embodiments, the system comprises: a processor programmed to: generate a feature vector including clinical variables and biomarker levels associated with the patient presenting with an unclassified adrenal mass; provide the feature vector to a machine learning model trained using a labeled feature vectors associated patients having adrenal masses classified as benign, adrenal cortical carcinoma, or another malignant adrenal mass; receive, from the trained machine learning model, an output indicative of a classification of the unclassified adrenal mass; and cause information indicative of the classification to be presented to a user to aid the user in classification of the unclassified adrenal mass.

In accordance with some embodiments, systems, methods, and media for automatically predicting a classification of incidental adrenal tumors based on clinical variables and urinary steroid levels are provided. In some embodiments, the system comprises: a processor programmed to: generate a feature vector including clinical variables and biomarker levels associated with the patient presenting with an unclassified adrenal mass; provide the feature vector to a machine learning model trained using a labeled feature vectors associated patients having adrenal masses classified as benign, adrenal cortical carcinoma, or another malignant adrenal mass; receive, from the trained machine learning model, an output indicative of a classification of the unclassified adrenal mass; and cause information indicative of the classification to be presented to a user to aid the user in classification of the unclassified adrenal mass.

Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

The technology disclosed relates to inter-model prediction score recalibration. In one implementation, the technology disclosed relates to a system including a first model that generates, based on evolutionary conservation summary statistics of amino acids in a target protein sequence, a first pathogenicity score-to-rank mapping for a set of variants in the target protein sequence; and a second model that generates, based on epistasis expressed by amino acid patterns spanning the target protein sequence and a plurality of non-target protein sequences aligned in multiple sequence alignment, a second pathogenicity score-to-rank mapping for the set of variants. The system also includes a reassignment logic that reassigns pathogenicity scores from the first set of pathogenicity scores to the set of variants based on the first and second score-to-rank mappings, and an output logic to generate a ranking of the set of variants based on the reassigned scores.

The technology disclosed relates to inter-model prediction score recalibration. In one implementation, the technology disclosed relates to a system including a first model that generates, based on evolutionary conservation summary statistics of amino acids in a target protein sequence, a first pathogenicity score-to-rank mapping for a set of variants in the target protein sequence; and a second model that generates, based on epistasis expressed by amino acid patterns spanning the target protein sequence and a plurality of non-target protein sequences aligned in multiple sequence alignment, a second pathogenicity score-to-rank mapping for the set of variants. The system also includes a reassignment logic that reassigns pathogenicity scores from the first set of pathogenicity scores to the set of variants based on the first and second score-to-rank mappings, and an output logic to generate a ranking of the set of variants based on the reassigned scores.

The present invention relates to systems and methods for improving the accuracy of disease diagnosis and to associated diagnostic tests involving the correlation of measured analytes with binary outcomes (e.g., not-disease or disease), as well as higher-order outcomes (e.g., one of several phases of a disease). Methods of the present invention use biomarker sets, preferably those with orthogonal functionality, to obtain concentration and proximity score values for disease and non-disease states. The biomarker set's proximity scores are graphed on an orthogonal grid, with one dimension for each biomarker. The proximity scores and orthogonal gridding is then used to calculate a disease state or non-disease state diagnosis for the patient.

The present invention relates to systems and methods for improving the accuracy of disease diagnosis and to associated diagnostic tests involving the correlation of measured analytes with binary outcomes (e.g., not-disease or disease), as well as higher-order outcomes (e.g., one of several phases of a disease). Methods of the present invention use biomarker sets, preferably those with orthogonal functionality, to obtain concentration and proximity score values for disease and non-disease states. The biomarker set's proximity scores are graphed on an orthogonal grid, with one dimension for each biomarker. The proximity scores and orthogonal gridding is then used to calculate a disease state or non-disease state diagnosis for the patient.

Embodiments of the present technology include a method for testing a blood sample for sepsis. The method may include receiving a blood sample from an individual. The method may also include executing an instruction to analyze the blood sample for sepsis. In addition, the method may include measuring values of a set of characteristics in the blood sample. The set of characteristics being determined prior to measuring the values. The method may further include analyzing the values of the set of characteristics to produce a representation of a suspicion of sepsis. In addition, the method may include displaying the representation. Embodiments also include systems for testing blood sample for sepsis.