The present invention is in the technical field of bioinformatics, and the implementation of bioinformatics. Advances in technology have led to a large increase in the rate at which data, in particular in the medical domain, can be generated (from patient sources, clinical trials, and research campaigns). The researcher is thus confronted with a large amount of information, and it is difficult to discover connections in the data, and thus to improve medical knowledge, even in spite of the amount of data available. The present application proposes to process and to structure medical data using a computer-implemented semantic network, enabling undiscovered connections between experiments and data sources to be made, and to continually add new data to the semantic network. In summary, it is proposed to provide a computer-implemented method and associated system which are able to automatically provide neurological knowledge model data by annotating neural connectivity data with further data sources.

The present invention is in the technical field of bioinformatics, and the implementation of bioinformatics. Advances in technology have led to a large increase in the rate at which data, in particular in the medical domain, can be generated (from patient sources, clinical trials, and research campaigns). The researcher is thus confronted with a large amount of information, and it is difficult to discover connections in the data, and thus to improve medical knowledge, even in spite of the amount of data available. The present application proposes to process and to structure medical data using a computer-implemented semantic network, enabling undiscovered connections between experiments and data sources to be made, and to continually add new data to the semantic network. In summary, it is proposed to provide a computer-implemented method and associated system which are able to automatically provide neurological knowledge model data by annotating neural connectivity data with further data sources.

Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.

Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.

In one aspect, a method is disclosed for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool. The method includes presenting, in a first screen of the GUI, a design space for a protein for an application, where the design space includes a set of sequences, where each sequence contains a respective set of activities pertaining to the application. The method also includes receiving, via a graphical element in the first screen, a selection of one or more query parameters of the design space, and presenting, in a second screen of the GUI, a solution space that includes a subset of the set of sequences, where each sequence contains the respective set of activities, where the subset of the set of sequences is selected based on the one or more query parameters.

In one aspect, a method is disclosed for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool. The method includes presenting, in a first screen of the GUI, a design space for a protein for an application, where the design space includes a set of sequences, where each sequence contains a respective set of activities pertaining to the application. The method also includes receiving, via a graphical element in the first screen, a selection of one or more query parameters of the design space, and presenting, in a second screen of the GUI, a solution space that includes a subset of the set of sequences, where each sequence contains the respective set of activities, where the subset of the set of sequences is selected based on the one or more query parameters.

Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods for large, multi-parameter data sets. Frequency difference gating compares at least two different data sets to identify regions in a multivariate space where a frequency of events from a first data set is different than a frequency of events from the second data set according to a defined threshold.

Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods for large, multi-parameter data sets. Frequency difference gating compares at least two different data sets to identify regions in a multivariate space where a frequency of events from a first data set is different than a frequency of events from the second data set according to a defined threshold.

A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.

A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.