Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Abstract: Computationally-efficient techniques facilitate secure pharmacological collaboration with respect to private drug target interaction (DTI) data. In one embodiment, a method begins by receiving, via a secret sharing protocol, observed DTI data from individual participating entities. A secure computation then is executed against the secretly-shared data to generate a pooled DTI dataset. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced data. The resulting pooled DTI dataset is then used to train a neural network model. The model is then used to provide one or more DTI predictions that are then returned to the participating entities (or other interested parties).

Abstract: Computationally-efficient techniques facilitate secure pharmacological collaboration with respect to private drug target interaction (DTI) data. In one embodiment, a method begins by receiving, via a secret sharing protocol, observed DTI data from individual participating entities. A secure computation then is executed against the secretly-shared data to generate a pooled DTI dataset. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced data. The resulting pooled DTI dataset is then used to train a neural network model. The model is then used to provide one or more DTI predictions that are then returned to the participating entities (or other interested parties).

Abstract: Computationally-efficient techniques facilitate secure crowdsourcing of genomic and phenotypic data, e.g., for large-scale association studies. In one embodiment, a method begins by receiving, via a secret sharing protocol, genomic and phenotypic data of individual study participants. Another data set, comprising results of pre-computation over random number data, e.g., mutually independent and uniformly-distributed random numbers and results of calculations over those random numbers, is also received via secret sharing. A secure computation then is executed against the secretly-shared genomic and phenotypic data, using the secretly-shared results of the pre-computation over random number data, to generate a set of genome-wide association study (GWAS) statistics. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced genomic data.

Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Abstract: Computationally-efficient techniques facilitate secure crowdsourcing of genomic and phenotypic data, e.g., for large-scale association studies. In one embodiment, a method begins by receiving, via a secret sharing protocol, genomic and phenotypic data of individual study participants. Another data set, comprising results of pre-computation over random number data, e.g., mutually independent and uniformly-distributed random numbers and results of calculations over those random numbers, is also received via secret sharing. A secure computation then is executed against the secretly-shared genomic and phenotypic data, using the secretly-shared results of the pre-computation over random number data, to generate a set of genome-wide association study (GWAS) statistics. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced genomic data.

Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Abstract: Computationally-efficient techniques facilitate secure pharmacological collaboration with respect to private drug target interaction (DTI) data. In one embodiment, a method begins by receiving, via a secret sharing protocol, observed DTI data from individual participating entities. A secure computation then is executed against the secretly-shared data to generate a pooled DTI dataset. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced data. The resulting pooled DTI dataset is then used to train a neural network model. The model is then used to provide one or more DTI predictions that are then returned to the participating entities (or other interested parties).

Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Abstract: Computationally-efficient techniques facilitate secure crowdsourcing of genomic and phenotypic data, e.g., for large-scale association studies. In one embodiment, a method begins by receiving, via a secret sharing protocol, genomic and phenotypic data of individual study participants. Another data set, comprising results of pre-computation over random number data, e.g., mutually independent and uniformly-distributed random numbers and results of calculations over those random numbers, is also received via secret sharing. A secure computation then is executed against the secretly-shared genomic and phenotypic data, using the secretly-shared results of the pre-computation over random number data, to generate a set of genome-wide association study (GWAS) statistics. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced genomic data.

Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.

Abstract: Methods and computer apparatuses are disclosed for processing genomic data in at least partially automated workflows of modules. A method comprises: specifying a source from which nucleic acid sequence(s) are to be obtained; selecting module(s) for processing data, including at least one module for processing the one or more nucleic acid sequences; presenting, in a graphical user interface, graphical components representing the source and the module(s) as nodes within a workspace; receiving, via the graphical user interface, inputs arranging the source and the module(s) as a workflow comprising a series of nodes, the series indicating, for each particular module, that output from one of the source or another particular module is to be input into the particular module; generating an output for the workflow based upon the nucleic acid sequence(s) by processing each module in an order indicated by the series.