The present invention generally relates to a process for designing a recombinant poxvirus for a therapeutic vaccine, i.e. personalized cancer vaccine, said recombinant poxvirus comprising one or more expression cassettes, each for expression of a fusion of a plurality of peptides, i.e. neopeptides, characterized in that it comprises performing by processing means (11) of a server (1) the steps of : (a) selecting a first subset of candidate peptides, wherein said peptides present transmembrane scores below a TMS threshold; b) determining an optimal distribution of the candidate peptides from said first subset to the expression cassette(s) among a plurality of possible distributions, wherein said optimal distribution presents, if there are at least two expression cassettes, the lowest range between the hydropathy scores of at least two expression cassettes; (c) for each expression cassette, determining an optimal slot allocation of the candidate peptides as function of cassette slot occupancy rule so as to select the peptide fusion with the lowest TM score; (d) determining a DNA transfer sequence comprising the nucleotide sequence of the one or more expression cassette(s) for generation of said recombinant poxvirus.

An incremental author disambiguation framework may create new clusters to accommodate new data based on the existing cluster results and newly added data. The proposed system may provide frequent update of taxonomic classification, name disambiguation and many other applications because it takes less time to generate new results. In addition, the proposed methods may reduce the time needed for updating the model and help improve the performance with the limited computational resource.

An incremental author disambiguation framework may create new clusters to accommodate new data based on the existing cluster results and newly added data. The proposed system may provide frequent update of taxonomic classification, name disambiguation and many other applications because it takes less time to generate new results. In addition, the proposed methods may reduce the time needed for updating the model and help improve the performance with the limited computational resource.

Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

Noise models for processing nucleic acid datasets can stratify processed sequence reads into different read tiers. Each read tier can be defined based on whether a potential variant location is at an overlapping region and/or a complementary region of the sequence reads. A processing system can determine, for each read tier, a stratified sequencing depth at the variant location. The processing system can determine, for reach read tier, one or more noise parameters conditioned on the stratified sequencing depth of the read tier. The noise parameters can be associated with a noise distribution. The processing system can generate an output for each noise model based on the noise parameters conditioned on the stratified sequencing depth. The processing system can combine the output for each stratified noise model to generate a combined result, which can represent a likelihood that an event would be as or more extreme than the observed data.

Deep RNA sequencing is a technology that provides an initial diagnostic for sepsis that can also monitor the indicia of treatment and recovery (bacterial counts reduce, physiology returns to steady-state). The invention can be used for many other hospital conditions, particularly those needing an intensive care unit stay with the attendant risk of bacterial infection, such as trauma, stroke, myocardial infarction, or major surgery.

Deep RNA sequencing is a technology that provides an initial diagnostic for sepsis that can also monitor the indicia of treatment and recovery (bacterial counts reduce, physiology returns to steady-state). The invention can be used for many other hospital conditions, particularly those needing an intensive care unit stay with the attendant risk of bacterial infection, such as trauma, stroke, myocardial infarction, or major surgery.