Computer-based systems for acquiring and analyzing observational subject data

A computer-implemented method of classifying a drug, comprising:

• obtaining observational data concerning an animal subject to which the drug is administered, the observational data comprising at least one of thermal data or respirational data, the observation data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component;
• predicting a class label of the drug by applying the features to a machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features;
• and
• providing an indication of the class label.

A computer-implemented method of classifying a psychedelic drug, comprising:

• obtaining observational data concerning an animal subject to which a predetermined dose of the psychedelic drug is administered, the observation data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component, the features comprising at least one of head twitch, nose scratch, ear scratch, head shake, body elongation, or elongation-contraction;
• predicting a class label of the psychedelic drug at the predetermined dose by applying the features to a machine-learning classifier component trained to predict the class label of the psychedelic drug from, at least in part, the features;
• and
• providing an indication of the class label.

A computer-implemented drug screening method, comprising:

• obtaining observational data concerning an animal subject, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting instant behavioral features from the observational data;
• creating a treatment signature, the treatment signature including higher-order features derived from the instant behavioral features using a first machine-learning component, the higher-order features including at least one of a state feature, a motif feature, or a domain feature;
• generating a target signature difference between the treatment signature and a baseline signature;
• identifying at least one reference drug or condition based on the target signature difference, identification comprising:
• generating an upregulation enrichment score and a downregulation enrichment score for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• generating a combined enrichment score for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• or
• generating a similarity value for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• and
• providing an indication of the similarity value, combined enrichment score, or upregulation and downregulation enrichment scores for the at least one reference drug or condition.

A computer-implemented method drug screening method, comprising:

• in a training phase:
• obtaining, for each first animal subject in three or more first sets of first animal subjects, each of the first sets corresponding to a combination of values of two or more characteristics of the first animals, a first value for each behavioral feature in a set of features, the features including:
• instant behavioral features extracted from observational data acquired for the first animal subjects using an enclosure instrumented with at least one sensing device;
• and
• higher-order features derived from the instant behavioral features using a first machine-learning component;
• determining, using a second machine-learning component and the first values, a mapping between at least two dimensions and corresponding functions of the features, the at least two dimensions including a treatment dimension and a secondary dimension;
• and
• in a screening phase:
• obtaining a second value for each behavioral feature in the set of the features for a second animal subject to which a compound is administered;
• determining, using the mapping and the second values, a treatment effect of the compound;
• and
• providing an indication of the treatment effect.

A computer-implemented method of classifying a drug comprising:

• obtaining EEG data from a plurality of electrodes positioned on an animal subject to which the drug is administered at a first dose;
• obtaining acceleration data from one or more accelerometers positioned on the animal subject to which the drug is administered;
• predicting a class label for the drug by applying the EEG data and the acceleration data to a machine-learning classifier component trained to predict the class label using the EEG data and the acceleration data;
• and
• providing an indication of the class label.

A computer-implemented method of extracting gait features of a rodent comprising:

• obtaining video data illustrating a rodent, to which a drug is administered, over a predetermined period, the video data acquired using an enclosure for the rodent, the enclosure instrumented with an illuminated track for the rodent and at least one imaging device positioned to image an underside of the illuminated track;
• annotating frames in the video data with labels using two machine-learning components, the labels including:
• a first one of the two machine-learning components configured to divide a frame in video data into segments corresponding to first object classes, the first object classes comprising a paw class;
• and
• a second one of the two machine-learning components configured to detect bounding boxes corresponding to second object classes, the second object classes including hind left, hind right, front left, and front right paws;
• generating segmented images using the annotating frames, the segmented images divided into segments corresponding to third object classes including hind left, hind right, front left, and front right paws;
• and
• extracting gait features of the rodent from the segmented images.

A system for classifying a drug, comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• obtaining observational data concerning an animal subject to which the drug is administered, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component;
• predicting a class label of the drug by applying the features to a machine-learning classifier component, the machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features;
• and
• providing an indication of the class label.

A system for classifying a drug, comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• obtaining observational data concerning an animal subject to which the drug is administered, the observational data comprising at least one of thermal data or respirational data, the observation data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component;
• predicting a class label of the drug by applying the features to a machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features;
• and
• providing an indication of the class label.

A system for classifying a psychedelic drug, comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• obtaining observational data concerning an animal subject to which a predetermined dose of the psychedelic drug is administered, the observation data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component, the features comprising at least one of head twitch, nose scratch, ear scratch, head shake, body elongation, or elongation-contraction;
• predicting a class label of the psychedelic drug at the predetermined dose by applying the features to a machine-learning classifier component trained to predict the class label of the psychedelic drug from, at least in part, the features;
• and
• providing an indication of the class label.

A system for drug screening, comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• obtaining observational data concerning an animal subject, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting instant behavioral features from the observational data;
• creating a treatment signature, the treatment signature including higher-order features derived from the instant behavioral features using a first machine-learning component, the higher-order features including at least one of a state feature, a motif feature, or a domain feature;
• generating a target signature difference between the treatment signature and a baseline signature;
• identifying at least one reference drug or condition based on the target signature difference, identification comprising:
• generating an upregulation enrichment score and a downregulation enrichment score for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• generating a combined enrichment score for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• or
• generating a similarity value for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• and
• providing an indication of the similarity value, combined enrichment score, or upregulation and downregulation enrichment scores for the at least one reference drug or condition.

A system for drug screening, comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• in a training phase:
• obtaining, for each first animal subject in three or more sets of first animal subjects, each of the first sets corresponding to a combination of values of two or more characteristics of the first animals, a first value for each behavioral feature in a set of features, the features including:
• instant behavioral features extracted from observational data acquired for the first animal subjects using an enclosure instrumented with at least one sensing device;
• and
• higher-order features derived from the instant behavioral features using a first machine-learning component;
• determining, using a second machine-learning component and the first values, a mapping between at least two dimensions and corresponding functions of the features, the at least two dimensions including a treatment dimension and a secondary dimension;
• and
• in a screening phase:
• obtaining a second value for each behavioral feature in the set of the features for a second animal subject to which a compound is administered;
• determining, using the mapping and the second values, a treatment effect of the compound;
• and
• providing an indication of the treatment effect.

A system of classifying a drug comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• obtaining EEG data from a plurality of electrodes positioned on an animal subject to which the drug is administered at a first dose;
• obtaining acceleration data from one or more accelerometers positioned on the animal subject to which the drug is administered;
• predicting a class label for the drug by applying the EEG data and the acceleration data to a machine-learning classifier component trained to predict the class label using the EEG data and the acceleration data;
• and
• providing an indication of the class label.

A system of extracting gait features of a rodent comprising:

• at least one processor;
• and
• a non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• obtaining video data illustrating a rodent, to which a drug is administered, over a predetermined period, the video data acquired using an enclosure for the rodent, the enclosure instrumented with an illuminated track for the rodent and at least one imaging device positioned to image an underside of the illuminated track;
• annotating frames in the video data with labels using two machine-learning components, the labels including:
• a first one of the two machine-learning components configured to divide a frame in video data into segments corresponding to first object classes, the first object classes comprising a paw class;
• and
• a second one of the two machine-learning components configured to detect bounding boxes corresponding to second object classes, the second object classes including hind left, hind right, front left, and front right paws;
• generating segmented images using the annotating frames, the segmented images divided into segments corresponding to third object classes including hind left, hind right, front left, and front right paws;
• and
• extracting gait features of the rodent from the segmented images.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for classifying a drug, the operations comprising:

• obtaining observational data concerning an animal subject to which the drug is administered, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component;
• predicting a class label of the drug by applying the features to a machine-learning classifier component, the machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features;
• and
• providing an indication of the class label.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for classifying a drug, the operations comprising:

• obtaining observational data concerning an animal subject to which the drug is administered, the observational data comprising at least one of thermal data or respirational data, the observation data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component;
• predicting a class label of the drug by applying the features to a machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features;
• and
• providing an indication of the class label.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for classifying a psychedelic drug comprising:

• obtaining observational data concerning an animal subject to which a predetermined dose of the psychedelic drug is administered, the observation data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting features by applying the observational data to a machine-learning feature-extraction component, the features comprising at least one of head twitch, nose scratch, ear scratch, head shake, body elongation, or elongation-contraction;
• predicting a class label of the psychedelic drug at the predetermined dose by applying the features to a machine-learning classifier component trained to predict the class label of the psychedelic drug from, at least in part, the features;
• and
• providing an indication of the class label.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for drug screening, the operations comprising:

• obtaining observational data concerning an animal subject, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device;
• extracting instant behavioral features from the observational data;
• creating a treatment signature, the treatment signature including higher-order features derived from the instant behavioral features using a first machine-learning component, the higher-order features including at least one of a state feature, a motif feature, or a domain feature;
• generating a target signature difference between the treatment signature and a baseline signature;
• identifying at least one reference drug or condition based on the target signature difference, identification comprising:
• generating an upregulation enrichment score and a downregulation enrichment score for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• generating a combined enrichment score for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• or
• generating a similarity value for the at least one reference drug or condition using the target signature difference and a reference signature difference corresponding to the one of the at least one reference drug or condition;
• and
• providing an indication of the similarity value, combined enrichment score, or upregulation and downregulation enrichment scores for the at least one reference drug or condition.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for drug screening, the operations comprising:

• in a training phase:
• obtaining, for each first animal subject in three or more sets of first animal subjects, each of the first sets corresponding to a combination of values of two or more characteristics of the first animals, a first value for each behavioral feature in a set of features, the features including:
• instant behavioral features extracted from observational data acquired for the first animal subjects using an enclosure instrumented with at least one sensing device;
• and
• higher-order features derived from the instant behavioral features using a first machine-learning component;
• determining, using a second machine-learning component and the first values, a mapping between at least two dimensions and corresponding functions of the features, the at least two dimensions including a treatment dimension and a secondary dimension;
• and
• in a screening phase:
• obtaining a second value for each behavioral feature in the set of the features for a second animal subject to which a compound is administered;
• determining, using the mapping and the second values, a treatment effect of the compound;
• and
• providing an indication of the treatment effect.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for classifying a drug, the operations comprising:

• obtaining EEG data from a plurality of electrodes positioned on an animal subject to which the drug is administered at a first dose;
• obtaining acceleration data from one or more accelerometers positioned on the animal subject to which the drug is administered;
• predicting a class label for the drug by applying the EEG data and the acceleration data to a machine-learning classifier component trained to predict the class label using the EEG data and the acceleration data;
• and
• providing an indication of the class label.

A non-transitory computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform operations for extracting gait features of a rodent, the operations comprising:

• obtaining video data concerning a rodent, to which a drug is administered, over a predetermined period, the video data acquired using an enclosure for the rodent, the enclosure instrumented with an illuminated track for the rodent and at least one imaging device positioned to image an underside of the illuminated track;
• annotating frames in the video data with labels using two machine-learning components, the labels including:
• a first one of the two machine-learning components configured to divide a frame in video data into segments corresponding to first object classes, the first object classes comprising a paw class;
• and
• a second one of the two machine-learning components configured to detect bounding boxes corresponding to second object classes, the second object classes including hind left, hind right, front left, and front right paws;
• generating segmented images using the annotating frames, the segmented images divided into segments corresponding to third object classes including hind left, hind right, front left, and front right paws;
• and
• extracting gait features of the rodent from the segmented images.