Abstract: A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.

Abstract: A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.

Abstract: A framework and interface for invoking and assimilating external algorithms and interacting with the algorithms in-session and real-time are described. Embodiments include reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. Embodiments include an entire discovery or diagnosis process executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.

Abstract: A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.

Abstract: A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.

Abstract: A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.