The techniques and/or systems described herein are directed to improvements in genomic prediction using homomorphic encryption. For example, a genomic model can be generated by a prediction service provider to predict a risk of a disease or a presence of genetic traits. Genomic data corresponding to a genetic profile of an individual can be batch encoded into a plurality of polynomials, homomorphically encrypted, and provided to a service provider for evaluation. The genomic model can be batch encoded as well, and the genetic prediction may be determined by evaluating a dot product of the genomic model data the genomic data. A genomic prediction result value can be provided to a computing device associated with a user for subsequent decrypting and decoding. Homomorphic encoding and encryption can be used such that the genomic data may be applied to the prediction model and a result can be obtained without revealing any information about the model, the genomic data, or any genomic prediction.

The behavior and/or internal activities of a microorganism can be simulated using a model of the microorganism. Such simulations can be used to determine the efficacy of treatments, disinfectants, antibiotics, chemotherapies, or other methods of interacting with the microorganism, or to provide some other information about the microorganism. Systems and methods are provided herein for fitting, refining, or otherwise improving such models in an automated fashion. Such systems and methods include performing whole-cell experiments to determine a correspondence between the predictions of such models and the actual behavior of samples of the microorganism. Such systems and methods also include, based on such determined correspondences, directly assessing determined discrete sets of properties of the microorganism and/or of constituents of the microorganism and updating parameters of the model corresponding to the properties of the discrete set such that the overall accuracy of the model is improved.

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.

In some embodiments, the present invention provides method of identifying compounds that bind to phosphoinositol 4-phosphate adaptor protein-2 (FAPP2), including the steps of computationally identifying a compound that binds to FAPP2 using the atomic coordinates of at least the amino acids which make up the substrate binding pocket of FAPP2. Also provided are methods of designing, selecting and/or optimizing a compound that binds to FAPP2.

The present invention is directed to water-soluble membrane proteins, methods for the preparation thereof and methods of use thereof.

A repeatable methodology for generation of a specific biological function library (data pool) and techniques for structuring queries that cluster and parse gene and protein alterations in individual patients and patient cohorts. Method enables analytical distinction between detectable changes in biological function and non-detectable changes in biological function using current diagnostic techniques and technologies.

Systems, methods and computer-readable media are described herein for determining a protein's most-likely structural alignment. A maximum likelihood algorithm is utilized that compares possible input protein structural translocations with a template protein. It then calculates the optimally superimposed position for each input protein utilizing a distance-based probability scoring algorithm that accurately manages extreme distances.

A method for implementing an adapted patient care for an individual suffering from liver fibrosis after assessing liver fibrosis progression in the individual, and thus determining whether the individual is a slow, medium or fast fibroser. Also, a method for treating an individual suffering from liver fibrosis and identified as a fast fibroser, which includes the steps of identifying the individual as a fast fibroser by assessing fibrosis progression and treating the individual by administering without delay at least one therapeutic agent for treating liver fibrosis, or for treating the underlying cause responsible for liver fibrosis, or both.

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.

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.