The invention is directed to methods for selecting a treatment option for an activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) subject, a germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL) subject, a primary mediastinal B cell lymphoma (PMBL) subject, a Burkitt lymphoma (BL) subject, or a mantle cell lymphoma (MCL) subject by analyzing digital gene expression data obtained from the subject, e.g., from a biopsy sample.

Provided herein are methods for design of engineered polypeptides that recapitulate molecular structure features of a predetermined portion of a reference protein structure, e.g., an antibody epitope or a protein binding site. A Machine Learning (ML) model is trained by labeling blueprint records generated from a reference target structure with scores calculated based on computational protein modeling of polypeptide structures generated by the blueprint records. The method may include training an ML model based on a first set of blueprint records, or representations thereof, and a first set of scores, each blueprint record from the first set of blueprint records associated with each score from the first set of scores. After the training, the machine learning model may be executed to generate a second set of blueprint records. A set of engineered polypeptides are then generated based on the second set of blueprint records.

Provided herein are methods for design of engineered polypeptides that recapitulate molecular structure features of a predetermined portion of a reference protein structure, e.g., an antibody epitope or a protein binding site. A Machine Learning (ML) model is trained by labeling blueprint records generated from a reference target structure with scores calculated based on computational protein modeling of polypeptide structures generated by the blueprint records. The method may include training an ML model based on a first set of blueprint records, or representations thereof, and a first set of scores, each blueprint record from the first set of blueprint records associated with each score from the first set of scores. After the training, the machine learning model may be executed to generate a second set of blueprint records. A set of engineered polypeptides are then generated based on the second set of blueprint records.

A method for preoperatively characterizing an individual patients biomechanic function in preparation of implanting a prosthesis is provided. The method includes subjecting a patient to various activities, recording relative positions of anatomy during said various activities, measuring force environments responsive to said patient's anatomy and affected area during said various activities, characterizing the patient's biomechanic function from said relative positions and corresponding force environments, inputting the measured force environments, relative positions of knee anatomy, and patient's biomechanic function characterization into one or more computer simulation models, inputting a computer model of the prosthesis into said one or more computer simulation models, and manipulating the placement of the prosthesis in the computer simulation using said patient's biomechanic function characterization and said computer model of the prosthesis to approximate a preferred biomechanical fit of the prosthesis.

A method for preoperatively characterizing an individual patients biomechanic function in preparation of implanting a prosthesis is provided. The method includes subjecting a patient to various activities, recording relative positions of anatomy during said various activities, measuring force environments responsive to said patient's anatomy and affected area during said various activities, characterizing the patient's biomechanic function from said relative positions and corresponding force environments, inputting the measured force environments, relative positions of knee anatomy, and patient's biomechanic function characterization into one or more computer simulation models, inputting a computer model of the prosthesis into said one or more computer simulation models, and manipulating the placement of the prosthesis in the computer simulation using said patient's biomechanic function characterization and said computer model of the prosthesis to approximate a preferred biomechanical fit of the prosthesis.

The present application mainly relates to specific methods for inferring activity of a cellular signaling pathway in tissue and/or cells of a medical subject based at least on expression levels of one or more target gene(s) of the cellular signaling pathway measured in an extracted sample of the tissue and/or cells of the medical subject, an apparatus comprising a digital compressor configured to perform such methods and a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such methods.

The present application mainly relates to specific methods for inferring activity of a cellular signaling pathway in tissue and/or cells of a medical subject based at least on expression levels of one or more target gene(s) of the cellular signaling pathway measured in an extracted sample of the tissue and/or cells of the medical subject, an apparatus comprising a digital compressor configured to perform such methods and a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such methods.

An apparatus for generating a protein-drug interaction prediction model according to an aspect includes a data collection unit configured to collect protein data, drug molecular data, and interaction data between a protein and a drug molecule, a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data, and a model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model.

An apparatus for generating a protein-drug interaction prediction model according to an aspect includes a data collection unit configured to collect protein data, drug molecular data, and interaction data between a protein and a drug molecule, a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data, and a model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model.

Provided herein are method of analyzing genomic and metabolomic data from fungi to identify relationships between biosynthetic gene clusters and mass spectrometric features of metabolites.