Abstract: Systems and methods can apply machine learning techniques to predict biological responses. One of the methods is performed by at least one processor executing executable logic including at least one machine learning model trained to predict biological responses. The method includes receiving first sequence data of a first molecular sequence, receiving second sequence data of a second molecular sequence, and predicting a biological response for the second molecular sequence based at least partly on the received first and second sequence data.

Abstract: Systems and methods can apply machine learning techniques to predict biological responses. One of the methods is performed by at least one processor executing executable logic including at least one machine learning model trained to predict biological responses. The method includes receiving first sequence data of a first molecular sequence, receiving second sequence data of a second molecular sequence, and predicting a biological response for the second molecular sequence based at least partly on the received first and second sequence data.

Abstract: A system for designing vaccines includes one or more processors, and computer storage storing executable computer instructions in which, when executed by the one or more processers, cause the one or more processors to perform one or more operations. The one or more operations include applying, to a first temporal sequence data set, a plurality of driver models configured to generate output data representing one or more molecular sequences. The one or more operations include, for each of the plurality of driver models, training the driver model. The one or more operations include selecting, based on one or more trained translational responses, a set of trained driver models of the plurality of driver models. The one or more operations include selecting, based on second translational response data, a subset of trained driver models of the set of trained driver models.

Abstract: A system for designing vaccines includes one or more processors, and computer storage storing executable computer instructions in which, when executed by the one or more processers, cause the one or more processors to perform one or more operations. The one or more operations include applying, to a first temporal sequence data set, a plurality of driver models configured to generate output data representing one or more molecular sequences. The one or more operations include, for each of the plurality of driver models, training the driver model. The one or more operations include selecting, based on one or more trained translational responses, a set of trained driver models of the plurality of driver models. The one or more operations include selecting, based on second translational response data, a subset of trained driver models of the set of trained driver models.

Abstract: Provided are modified influenza virus subtype 2 neuraminidase molecules lacking all or substantially all of the stalk region that form active, soluble tetrameric neuraminidase when expressed in host cells and vaccine compositions comprising the tetrameric neuraminidase or a nucleic acid encoding the modified monomeric influenza virus subtype 2 neuraminidase molecules that forms tetrameric NA when expressed in a cell. Also provided are methods of using the vaccine compositions to vaccinate or immunize a subject against influenza virus.

Abstract: Disclosed herein are hybrid multivalent vaccine or immunogenic compositions comprising (i) one or more influenza virus proteins selected from one or more influenza virus hemagglutinin (HA) proteins, one or more influenza virus neuraminidase (NA) proteins, or a combination thereof; and (ii) one or more ribonucleic acid molecules encoding one or more influenza virus proteins selected from one or more influenza virus HA proteins, one or more influenza virus NA proteins, or a combination thereof. Also disclosed are methods of using the vaccine or immunogenic compositions.

Abstract: Disclosed herein are multivalent vaccine or immunogenic compositions comprising one or more recombinant influenza virus hemagglutinin (HA), one or more recombinant influenza virus neuraminidase (NA), and an optional adjuvant. Also disclosed are methods of using the vaccine or immunogenic composition.

Abstract: Disclosed are multivalent vaccine or immunogenic compositions comprising influenza virus hemagglutinin (HA) from standard of care influenza virus strains, or ribonucleic acid molecules encoding the same; and one or more influenza virus HA identified or designed by machine learning, or one or more ribonucleic acid molecules that encode the influenza virus HA identified or designed by machine learning. Also disclosed are methods of using the vaccine or immunogenic compositions.

Abstract: Systems and methods can apply machine learning techniques to predict biological responses. One of the methods is performed by at least one processor executing executable logic including at least one machine learning model trained to predict biological responses. The method includes receiving first sequence data of a first molecular sequence, receiving second sequence data of a second molecular sequence, and predicting a biological response for the second molecular sequence based at least partly on the received first and second sequence data.

Abstract: A system for designing vaccines includes one or more processors, and computer storage storing executable computer instructions in which, when executed by the one or more processers, cause the one or more processors to perform one or more operations. The one or more operations include applying, to a first temporal sequence data set, a plurality of driver models configured to generate output data representing one or more molecular sequences. The one or more operations include, for each of the plurality of driver models, training the driver model. The one or more operations include selecting, based on one or more trained translational responses, a set of trained driver models of the plurality of driver models. The one or more operations include selecting, based on second translational response data, a subset of trained driver models of the set of trained driver models.