Machine training and application of machine-trained classifier are used for image-based tumor phenotyping in a medical system. To create a training database with known phenotype information, synthetic medical images are created. A computational tumor model creates various examples of tumors in tissue. Using the computational tumor model allows one to create examples not available from actual patients, increasing the number and variance of examples used for machine-learning to predict tumor phenotype. A model of an imaging system generates synthetic images from the examples. The machine-trained classifier is applied to images from actual patients to predict tumor phenotype for that patient based on the knowledge learned from the synthetic images.

Methods for non-invasive assessment of genetic variations that make use of nucleic acid fragment length information, in particular length of fragments in circulating cell-free nucleic acids and compares the number of counts from fragments with different length.

It is an invention aimed at providing methods for optimizing read information mapped to the particular gene loci such as MHC loci under a framework of probabilistic statistical processing. In the present invention, a step in which for all reads, calculation of the expected number of mappings to alleles of the particular gene loci is performed for read information in which mapping of reads to alleles of the particular gene loci is identified, a step in which the total number of expected mappings for each allele is calculated, and a step in which the fraction of reads allocated to each allele is calculated, are repeatedly executed, in which the optimization of read information is performed in a computer, and based on the optimized information, a method of easily and accurately estimating the genotypes of the particular gene loci, a computer system, and a computer program capable of easily and accurately estimating the genotype of the particular gene loci, are provided.

Methods for identifying and classifying differences between biological samples are based on replication timing (RT) data. By comparing RT data for a test sample(s) to RT data for already characterized samples, one can identify differences and profile any new cell type or disease. These new methods allow for the detection of all the changes between distinct samples, many of which would escape detection by previous methods that discard any features showing any intra-sample variation.

A method for employee biometric tracking is provided. The method comprises providing to a user a plurality of wearable devices capable of being connected to the user, establishing a wireless connection between the plurality of wearable devices and a mobile device, collecting by the plurality of wearable devices a plurality of biometric data from the user, receiving by an application stored on the mobile device the plurality of biometric data, inputting into a predictive engine biometric data selected from the plurality of biometric data, determining by the predictive engine in response to the biometric data whether the user is at, or soon will be at, an alert level, creating an alert signal, and displaying the alert signal to the user.

The present invention relates to a method of diagnosing Parkinson's disease in a subject using a novel set of biomarkers. The invention further includes compositions, methods and uses of a novel set of biomarkers to assess the risk of developing Parkinson's disease, to provide pre-symptomatic diagnosis of Parkinson's disease, and to assess prognosis of Parkinson's disease following therapeutic or other intervention.

The disclosure relates to the use of steganographic methods to camouflage information encoded on nucleic acids. Specifically, the method comprising preparing a pool of recombinant nucleic acid constructs, wherein at least one of the constnjcts comprises the nucleic acid sequence to be camouflaged and wherein the pool is heterogeneous with respect to the orientation of the nucleic acid sequence to be camouflaged, and the information is camouflaged using genetic recombination.

The present invention relates to the unexpected discovery of methods and devices that can be used for high-throughput precise quantification, detection and/or temporal profiling of cellular secretions. In various embodiments, the methods of the invention allow for high-throughput absolute detection of secretions of cells, identification of the nature of the secreted molecules, and/or the nature of the secreting cells. Further, the present invention includes a device combining microfluidics and antibody printing, wherein the device can be used to detect protein secretion signature of cells in a high-throughput manner. Further, the present invention includes compositions comprising molecules that can be used to reduce cell death and to implement cell-less therapies. Further, the present invention includes a method for training an algorithm to predict temporal profile of cellular secretion.

To provide methods of creating trait prediction models for predicting phenotypes of traits from single nucleotide polymorphism data and methods of predicting traits with which traits can he predicted with a high accuracy.

This is a method of creating a trait prediction model for predicting a phenotype of a multifactorial trait using data of a plurality of single nucleotide polymorphisms linked to a trait for each of a plurality of individuals of an organism: representing each of the plurality of single nucleotide polymorphisms as a matrix; classifying the plurality of single nucleotide polymorphisms into a plurality of categories based on their genetic architectures; calculating, for each of the categories, a genomic similarity matrix using the represented matrix and the number of the single nucleotide polymorphisms belonging to the category; and applying the genomic similarity matrix and a parameter of the genetic architecture to a linear mixed model.

There is provided a computer-implemented method and apparatus for determining a likelihood of occurrence of a medical event for a subject. A risk profile for the subject is acquired and a plurality of risk profiles for other subjects are obtained from a database. The acquired subject risk profile is compared to the obtained plurality of other subject risk profiles. At least one risk profile is selected from the obtained plurality of other subject risk profiles that most closely matches the acquired subject risk profile. The likelihood of occurrence of a medical event for the subject is determined based on the selected at least one risk profile. A signal indicative of the determined likelihood of occurrence of the medical event for the subject is output.