A method of diagnosing central nervous system injuries such as acquired brain injury (ABI) and/or acquired spinal cord injury (ASI), including mild TBI (concussion or blast wave), mild ASI (contusion, stretch or partial cord transection), non-TBI brain injury and/or non-TSI spinal cord injury in a subject (animal or human). The method includes (a) obtaining a biological test sample from the subject, identifying metabolites in the subject's sample using metabolomics thereby obtaining a subject's metabolite matrix and generating a subject's profile using the patient's metabolite matrix; and (b) using multivariate statistical analysis and machine learning to compare the subject's profile with predetermined set of profiles of CNS injuries and a predetermined set of profiles of controls to determine if the subject has a CNS injury.

At least one binding site of a protein is probed by calculating a set of molecular dynamic trajectories of a protein-ligand complex family. At least one script is applied to the molecular dynamic trajectories to form a set of tensors, and at least one second script is applied to the set of tensors to integrate the set of tensors with experimental binding data corresponding to the protein-ligand complex family to form a primary image of the binding site, thereby probing the binding site of the protein.

A system and method that receive a distance matrix for multiple patients and a patient of interest, assign a radial distance value between the patient of interest and the other patients based on the distance matrix value for each of the multiple patients, generate an angular distance value between the multiple patients based at least in part on a measure of similarity between each patient, and minimize a cost function based at least in part on the angular distance value between each patient and each other patient. Minimizing the cost function may include calculating a patient contribution to the cost function for a plurality of angular distance values and selecting the angular distance value with the smallest patient contribution. The processor also may be configured to generate and display a radar plot based on the assigned radial distance value and generated angular distance value of each patient.

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

The present invention relates to a computer-implemented method for modelling genomic data represented in an unsupervised neural network, trVAE, comprising a conditional variational autoencoder, CVAE, with an encoder (f) and a decoder (g).

The present invention relates to a computer-implemented method for modelling genomic data represented in an unsupervised neural network, trVAE, comprising a conditional variational autoencoder, CVAE, with an encoder (f) and a decoder (g).

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

A computer-implemented method of identifying a tool compound is provided. The method comprises: searching a database for first candidate compounds that each target one or more first target genes; generating a first fingerprint for each first candidate compound by: searching the database for genes associated with the first candidate compound, and predicting genes associated with the first candidate compound; and filtering the first candidate compounds using the first fingerprints to identify a first optimum compound for targeting the one or more first target genes.

A computer-implemented method of identifying a tool compound is provided. The method comprises: searching a database for first candidate compounds that each target one or more first target genes; generating a first fingerprint for each first candidate compound by: searching the database for genes associated with the first candidate compound, and predicting genes associated with the first candidate compound; and filtering the first candidate compounds using the first fingerprints to identify a first optimum compound for targeting the one or more first target genes.