Method for acquiring and analysing the fine motor skills of an individual. Presenting a course on a medium and inviting the individual to make a free movement with at least one finger and/or one accessory on the medium, this movement being linked to the course. Recording a period of time taken to complete at least part of the course. Recording successive positions of the finger and/or the accessory during the completion of at least a part of the course. Analysing the recordings in order to generate at least one random variable describing successive positions according to a predefined statistical model. Generating a score representative of the fine motor skills, based on at least the period of time taken to complete at least part of the course and a statistical measurement of the random variable, characteristic of a quantity of information, disorder or chaos in the recording of the successive positions.

Provided herein are methods of identifying gene expression levels in specific cell types based on bulk gene expression levels measured in tissue samples comprising a plurality of cell types.

Provided herein are methods of identifying gene expression levels in specific cell types based on bulk gene expression levels measured in tissue samples comprising a plurality of cell types.

A system includes a memory storing a user profile for a user of the system and machine-readable instructions and a control system including one or more processors configured to execute the machine-readable instructions to receive physiological data associated with the user during a sleep session, determine, based at least in part on the received physiological data, a set of sleep-related parameters for the sleep session, subsequent to the sleep session, select one of the set of sleep-related parameters as a targeted parameter, the selection of the targeted parameter being based at least in part on the stored user profile, the set of sleep-related parameters, or both, and cause information to be communicated to the user via a user device, the information being indicative of the targeted parameter, a recommendation associated with improving the targeted parameter for the user in one or more subsequent sleep sessions, or both.

Methods and arrays for identifying the cell or tissue origin of DNA are provided. Accordingly there is provided a method of identifying DNA having a methylation pattern distinctive of a cell or tissue type or state comprising: labeling an epigenetic modification of interest in a DNA sample with a label; contacting said sample on an array comprising a plurality of probes for said DNA under conditions which allow specific hybridization between said plurality of probes and said DNA; and detecting said hybridization, wherein an amount of said label is indicative of the cell or tissue type or state, wherein the method is effected in the absence of amplification of said DNA.

Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

A method comprising receiving, for each of a plurality of subjects having a specified type of disease and receiving a specified therapy for treating the disease, a first biological signature obtained pre-treatment and a second biological signature obtained on-treatment; calculating, for each of the plurality of subjects, a set of values representing a ratio between the first and second biological signatures associated with the respective subject; at a training stage, training a machine learning model on a training set comprising: (i) the calculated sets of values, and (ii) labels associated with an outcome of the specified therapy in each of the subjects; to generate a classifier suitable for predicting a response in a target patient to said specified therapy.