G06F19/12

BIOMARKERS OF ENDOGENOUS BIOLOGICAL TIME
20180357360 · 2018-12-13 ·

Provided herein are biomarkers of endogenous biological time (e.g. circadian time). In particular compositions and methods are provided for assessing the biological time of a subject, and diagnosis of diseases/conditions and/or providing treatments based thereon.

SYSTEMS AND METHODS FOR IDENTIFYING CANCER TREATMENTS FROM NORMALIZED BIOMARKER SCORES
20180358128 · 2018-12-13 ·

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

MODELING OF SYSTEMATIC IMMUNITY IN PATIENTS

In one implementation, a computer-implemented method includes accessing patient-derived blood data; identifying biomarker pair interactions based on signal processing of the patient-derived blood data; generating a data model that characterizes one or more aspects of human immune system interactions based on reverse engineering of the human immune system interactions using the identified biomarker pair interactions; evaluating suitability of the data model, wherein the evaluating includes: statistically testing the data model in characterizing correlations between biomarker pairs; decomposing the data model to determine (i) model order accuracy, (ii) whether biomarker nodes act in together or opposite each other, and (iii) normalized data with dynamic effects of and providing the data model for use in treatment-based decision making based on the evaluating.

Method and system for image processing and patient-specific modeling of blood flow
10149723 · 2018-12-11 · ·

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.

METHODS AND COMPOSITIONS THAT UTILIZE TRANSCRIPTOME SEQUENCING DATA IN MACHINE LEARNING-BASED CLASSIFICATION

Provided herein are methods and systems for producing a modified biological dataset by flagging or removing a nucleic acid sequence from the biological dataset that is assigned a noise-call to produce the modified biological dataset. The noise-call may be based on comparing a gene expression level, sequence information, or a combination thereof with a nucleic acid sequence of a control sample.

CANCER ANTIGEN TARGETS AND USES THEREOF

The presently disclosed subject matter provides methods and compositions for treating myeloid disorders (e.g., acute myeloid leukemia (AML)). It relates to immunoresponsive cells bearing antigen recognizing receptors (e.g., chimeric antigen receptors (CARs)) targeting AML-specific antigens.

System and method for digital tooth imaging
10143537 · 2018-12-04 · ·

Method and system for managing multiple impressions of a patient's jaw for an orthodontic treatment is provided. The method includes scanning at least a first impression and a second impression of same jaw for the orthodontic treatment; determining if the first jaw impression and the second jaw impression have distortion in different areas; selecting the first jaw impression or the second jaw impression as a base impression; and replacing a distorted tooth data from the base impression with data for the same tooth from a non-base impression. The method also includes scanning at least a first jaw impression for the orthodontic treatment; scanning a bite impression for the orthodontic treatment; matching the scanned first jaw impression with the scanned bite impression; comparing bite information with a tooth occlusal surface; and determining if reconstruction is to be performed on the tooth occlusal surface.

SYNCHRONIZATION SCHEME FOR PHYSICS SIMULATIONS

A server, which is in communication with a plurality of client computing devices configured to perform a reduced simulation function, comprises a synchronization engine configured to generate synchronization packets for one or more rigid bodies according to a synchronization scheme and, for each rigid body, to dynamically update the synchronization scheme based on a current state of the rigid body in simulation data and stored states for the rigid body which are stored in a buffer. The synchronization packets are then transmitted to one of the plurality of client computing devices.

Genome and self-evolution of AI
20180330048 · 2018-11-15 ·

The components and structure for a genome created for the purpose of the evolutionary development of artificial intelligence systems/machines without human intervention.

GENOME-WIDE ASSOCIATION STUDY METHOD FOR IMBALANCED SAMPLES
20180330057 · 2018-11-15 ·

The present disclosure provides a genome-wide association study method for imbalanced samples, including: randomly selecting L subsets from the healthy samples; pairing each of the L subsets with the diseased samples to obtain L sample combinations, and determining key genetic loci corresponding to each sample combination; evaluating a score of an importance degree of each sample combination according to times that each key genetic locus is determined in the L sample combinations; for each healthy sample, determining a mean value of scores of an importance degree of sample combinations that the healthy sample is assigned to, and determining the mean value as a confidence score of the healthy sample; and normalizing the confidence score of each healthy sample to obtain a weight of each healthy sample, and performing weighted logistic regression according to the weight of each healthy sample.