The invention provides systems and methods for encoding specific portions of a genomic graph in a way that is useful for copying or moving those portions from one location to another. The genomic graph can comprise nodes connected by edges, wherein the nodes and edges are stored as edge objects in non-transitory memory of a computer system and wherein an edge within the graph represents a genomic sequence. In on embodiment, the a method of encoding a genomic graph comprises selecting at least one edge from the genomic graph, the at least one edge representing a single genetic sequence and having a position on the genomic graph. The method can further comprise encoding the at least one edge as a string of bits, in which at least a first component of the string of bits stores at least a portion of the position of the edge on the genomic graph.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems in a flow cytometer having an interrogation zone and illumination impinging the interrogation zone include: a lens subsystem including a collimating element that collimates light from the interrogation zone, a light dispersion element that disperses collimated light into a light spectrum, and a focusing lens that focuses the light spectrum onto an array of adjacent detection points; a detector array, including semiconductor detector devices, that collectively detects a full spectral range of input light signals, in which each detector device detects a subset spectral range of the full spectral range of light signals; and a user interface that enables a user to create a set of virtual detector channels by grouping detectors in the detector array, such that each virtual detector channel corresponds to a detector group and has a virtual detector channel range including the sum of subset spectral ranges of the detectors in the corresponding detector group.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Techniques for presenting a dynamic graphical user interface (GUI) for selecting multiple non-conflicting filters to be used to filter performance data for display in a visualization are provided.

A method for annotating a protein sequence or a subsequence thereof includes the steps of providing an input protein sequence or a subsequence thereof. The subsequence is defined as a central node of a graph or protein network. A subgraph of the graph is calculated including the central node, according to a predefined radius and weights and/or resistances of edges of the subgraph are also calculated. Annotated nodes in the subgraph are identified. Resistance values between the central nodes and each of the annotated nodes in the subgraph are calculated and a list of annotated nodes is outputted. Each of the annotated nodes has a characteristic calculated resistance value to the central node of the input protein sequence.

Various methods, systems, computer readable media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer readable media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject; identifying an MF profile cluster with which to associate the MF profile for the subject; and clustering the plurality of MF profiles to obtain the MF profile clusters.

Various methods, systems, computer readable media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer readable media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample from a plurality of subjects, determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects, and storing the plurality of MF profiles in association with information identifying the particular cancer type.

Various methods, systems, computer readable media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer readable media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for biological samples; determining a respective plurality of molecular-functional (MF) profiles for a plurality of subjects; clustering the plurality of MF profiles to obtain MF profile clusters; determining a molecular-functional (MF) profile for an additional subject; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

Various methods, systems, computer readable media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer readable media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample; determining a molecular-functional (MF) profile for the subject at least in part by determining first and second visual characteristics for first and second GUI elements using the data; generating a personalized GUI personalized to the subject using the first and second visual characteristics; and presenting the generated personalized GUI to a user.