Systems, media, compositions, methods, and kits disclosed herein relate to a panel of autoantibody biomarkers for the early detection of colon cell proliferative disorders, including colorectal cancer. The presence or levels of the autoantibodies in a biological sample for the autoantibody panels described herein may be used for classifier generation, and as inputs in machine learning models useful to classify subjects in a population for the detection of colon cell proliferative disorders.

Systems, media, compositions, methods, and kits disclosed herein relate to a panel of autoantibody biomarkers for the early detection of colon cell proliferative disorders, including colorectal cancer. The presence or levels of the autoantibodies in a biological sample for the autoantibody panels described herein may be used for classifier generation, and as inputs in machine learning models useful to classify subjects in a population for the detection of colon cell proliferative disorders.

Disclosed herein is method and a system for predicting mutations in Ribonucleic acid (RNA) strains. In an embodiment, a similarity between a new viral RNA strain and reference RNA strains is determined. Further, a strain score for the new viral RNA strain is calculated based on the similarity between the new viral RNA strain and the reference RNA strains. Subsequently, mutation sites for the new viral RNA strain are identified by generating spatial nearness data corresponding to the reference RNA strains based on comparison between the strain score of the new viral RNA strain and the reference RNA strains. Finally, mutations of the new viral RNA strain are predicted by performing a generative modelling of a sequence of the new viral RNA strain with reference to the mutation sites of the new viral RNA strain.

Disclosed herein is method and a system for predicting mutations in Ribonucleic acid (RNA) strains. In an embodiment, a similarity between a new viral RNA strain and reference RNA strains is determined. Further, a strain score for the new viral RNA strain is calculated based on the similarity between the new viral RNA strain and the reference RNA strains. Subsequently, mutation sites for the new viral RNA strain are identified by generating spatial nearness data corresponding to the reference RNA strains based on comparison between the strain score of the new viral RNA strain and the reference RNA strains. Finally, mutations of the new viral RNA strain are predicted by performing a generative modelling of a sequence of the new viral RNA strain with reference to the mutation sites of the new viral RNA strain.

The present disclosure relates to a biologics development platform that derives biologics from aptamers found to bind to a target. Particularly, aspects of the present disclosure are directed to generating sequencing data and analysis data for each unique aptamer of an aptamer library that binds to a target within a monoclonal compartment, inferring aptamer sequences derived from the sequencing data and the analysis data, identifying interaction points between the aptamer sequences and epitopes of the target based on structure or sequence motifs of the aptamer sequences, modeling molecular dynamics of interactions between the aptamer sequences and the epitopes to identify characteristics of the interaction points as requirements or restraints for the interactions, and inferring one or more amino acid sequences based on the characteristics of the interaction points derived from the interactions between aptamer sequences and the epitopes.

Disclosed herein are methods of selecting an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer. Also disclosed are methods of selecting a donor from whom to derive an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer.

Disclosed herein are methods of selecting an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer. Also disclosed are methods of selecting a donor from whom to derive an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer.

Disclosed herein are methods of selecting an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer. Also disclosed are methods of selecting a donor from whom to derive an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer.

Disclosed herein are methods of selecting an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer. Also disclosed are methods of selecting a donor from whom to derive an allogeneic T cell line for therapeutic administration to a patient having or suspected of having a pathogen or cancer.

The present disclosure provides compositions and methods for the generation of an antibody or immunogenic composition, such as a vaccine, through epitope focusing by variable effective antigen surface concentration. Generally, the composition and methods of the disclosure comprise three steps: a “design process” comprising one or more in silico bioinformatics steps to select and generate a library of potential antigens for use in the immunogenic composition; a “formulation process”, comprising in vitro testing of potential antigens, using various biochemical assays, and further combining two or more antigens to generate one or more immunogenic compositions; and an “administering” step, whereby the immunogenic composition is administered to a host animal, immune cell, subject or patient. Further steps may also be included, such as the isolation and production of antibodies raised by host immune response to the immunogenic composition.