Abstract: Methods and systems for multiscale analysis and visualization of clinical data are provided. An example method includes receiving vectors of outcomes of trial subjects, generating a plurality of metric graphs including first nodes corresponding to the vectors of outcomes and selectively connected based on a first criterion, selecting, from the plurality of metric graphs and based on a second criterion, an optimal graph, generating, based on the optimal graph and a resolution parameter, a clustered graph including second nodes corresponding to groups of the first nodes, the second nodes being selectively connected based on a third criteria, generating a first layout of the clustered graph, the first layout including a two-dimensional (2D) representation of the clustered graph, generating, partially based on the first layout, a second layout of the optimal graph, the second layout including a 2D representation of the optimal graph, and displaying the second layout.

Abstract: A system and method for graph-based discovery of geometry of clinical data are disclosed. The method includes receiving vectors of outcomes of trial subjects; generating, based on the vectors of outcomes, a plurality of metric graphs such that each of the metric graphs includes a same set of nodes corresponding to the vectors of outcomes and the nodes are selectively connected based on a first criterion, the first criterion being based on a set of parameters, the set of parameters being uniquely selected, from a plurality of sets of parameters, for each of the metric graphs; determining, based on the plurality of metric graphs and a second criterion, an aggregated graph and a subset of the plurality of metric graphs; selecting, from the subset of the plurality of metric graphs and based on a third criterion, optimal and most representative graphs; and displaying a graphical representation of optimal graphs.

Abstract: A system and method for graph-based discovery of geometry of clinical data are disclosed. The method includes receiving vectors of outcomes of trial subjects; generating, based on the vectors of outcomes, a plurality of metric graphs such that each of the metric graphs includes a same set of nodes corresponding to the vectors of outcomes and the nodes are selectively connected based on a first criterion, the first criterion being based on a set of parameters, the set of parameters being uniquely selected, from a plurality of sets of parameters, for each of the metric graphs; determining, based on the plurality of metric graphs and a second criterion, an aggregated graph and a subset of the plurality of metric graphs; selecting, from the subset of the plurality of metric graphs and based on a third criterion, optimal and most representative graphs; and displaying a graphical representation of optimal graphs.

Abstract: Methods and systems for graph-based discovery of geometry of clinical data are provided. An example method includes receiving vectors of outcomes of trial subjects, generating, based on the vectors of outcomes, a plurality of metric graphs, each of the metric graphs including a set of nodes corresponding to the vectors of outcomes and a set of edges, performing an automatic search to identify communities of nodes in the optimal graph, displaying a graphical representation of the optimal graph and highlighting nodes in the graphical representation, the nodes corresponding to the community of nodes. Generating the set of edges includes selecting metrics and projection rules to obtain projections of the vectors of outcomes, and selectively connecting nodes based on determination that projections of corresponding vectors of outcomes belong to the same domain of a set of overlapping domains and a certain cluster within the domain.

Abstract: Methods and systems for graph-based discovery of geometry of clinical data are provided. An example method includes receiving vectors of outcomes of trial subjects, generating, based on the vectors of outcomes, a plurality of metric graphs, each of the metric graphs including a set of nodes corresponding to the vectors of outcomes and a set of edges, performing an automatic search to identify communities of nodes in the optimal graph, displaying a graphical representation of the optimal graph and highlighting nodes in the graphical representation, the nodes corresponding to the community of nodes. Generating the set of edges includes selecting metrics and projection rules to obtain projections of the vectors of outcomes, and selectively connecting nodes based on determination that projections of corresponding vectors of outcomes belong to the same domain of a set of overlapping domains and a certain cluster within the domain.