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.

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.

Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

Systems and methods to automatically de novo assemble a set of unordered read sequences into one or more, larger nucleotide sequences are presented. The method involves first creating two identical sets of the reads, dividing each read in both sets into smaller sorted mer sequences and then comparing the mers for each read in set 1 to the mers from each read in set 2 to exhaustively identify overlapping segments. Overlap information is used to construct a modified assembly string graph, traversal of which produces a sorted string graph layout file consisting of all the reads ordered left to right including their approximate starting offset position. The sorted string graph layout file is then processed by a novel multiple sequence alignment system that uses mer matches between all the overlapping reads at a given position to place matching individual bases from each read into columns from which an overall consensus sequence is determined.

Systems and methods to automatically de novo assemble a set of unordered read sequences into one or more, larger nucleotide sequences are presented. The method involves first creating two identical sets of the reads, dividing each read in both sets into smaller sorted mer sequences and then comparing the mers for each read in set 1 to the mers from each read in set 2 to exhaustively identify overlapping segments. Overlap information is used to construct a modified assembly string graph, traversal of which produces a sorted string graph layout file consisting of all the reads ordered left to right including their approximate starting offset position. The sorted string graph layout file is then processed by a novel multiple sequence alignment system that uses mer matches between all the overlapping reads at a given position to place matching individual bases from each read into columns from which an overall consensus sequence is determined.

Disclosed is a genome reconstruction method using whole genome data. According to the present invention, the genome reconstruction method reduces detection errors by converting a nucleotide sequence having a structural variation into a graph form, and then reconstructing the graph so that the structural variation and the copy number variation have consistent values. Thereafter, the genome arrangement form was restored by constructing a haplotype graph using heterozygous single nucleotide polymorphism information and then finding an Eulerian path with a minimum entropy value.

Disclosed is a genome reconstruction method using whole genome data. According to the present invention, the genome reconstruction method reduces detection errors by converting a nucleotide sequence having a structural variation into a graph form, and then reconstructing the graph so that the structural variation and the copy number variation have consistent values. Thereafter, the genome arrangement form was restored by constructing a haplotype graph using heterozygous single nucleotide polymorphism information and then finding an Eulerian path with a minimum entropy value.

Methods for identifying cancer patients amenable to anti-cancer immunotherapy are provided along with methods of monitoring cancer therapy. Also provided are methods of treating cancer patients amenable to anti-cancer immunotherapy. The methods involve determining the level of CD127 <low> PD-1 <low> T cells. The patients are treated with an immune checkpoint inhibitor, such as an anti-CTLA-4 antibody, e.g. ipilimumab.

Methods for identifying cancer patients amenable to anti-cancer immunotherapy are provided along with methods of monitoring cancer therapy. Also provided are methods of treating cancer patients amenable to anti-cancer immunotherapy. The methods involve determining the level of CD127 <low> PD-1 <low> T cells. The patients are treated with an immune checkpoint inhibitor, such as an anti-CTLA-4 antibody, e.g. ipilimumab.