Abstract: A processor may receive DNA data associated with a DNA sequence. The processor may classify the DNA sequence as exhibiting circadian behavior utilizing a graph deep learning algorithm. The graph deep learning algorithm may be trained utilizing a combination of features of a human interactome, features of DNA sequences associated with genes identified as exhibiting circadian behavior, and features of DNA sequences associated with genes identified as not exhibiting circadian behavior.

Abstract: A method, computer system, and a computer program product for predicting animal testing measurement levels is provided. The present invention may include referencing a human gene expression profiles data obtained under in vitro chemical treatment. The present invention may include referencing an animal measurements data obtained under in vivo chemical treatment. The present invention may include assigning an animal measurement level to a human gene expression profile by matching at least one identified compound. The present invention may include creating a chemical fingerprint of the identified compound. The present invention may include creating a machine learning feature space as a function of a human gene expression feature and a chemical fingerprint feature of the identified compound for predicting an associated animal measurement level assigned to the at least one human gene expression profile. The present invention may include training a model using the machine learning feature space.

Abstract: Classifying genetic sequences by receiving genetic sequence data according to sequence features associated with gene expression, determining a genetic sequence feature set, determining a first classification for the genetic sequence feature set according to a machine learning model, defining a causal feature set associated with the first classification for the genetic sequence according to the machine learning model, altering the causal feature set for the genetic sequence, yielding an altered causal feature set, determining a second classification for the altered causal feature set according to the machine learning model, wherein the second classification differs from the first classification, and defining a set of target features, wherein the target features include causal features the altered causal feature set.

Abstract: Evaluating certain risks that are involved with respect to the transmission of pathogens in a given physical space. Some aspects of the present invention include applying privacy-preserving artificial intelligence (AI) algorithms and physics-based simulations on image data to characterize the potential source of pathogens. A multi-outcome artificial intelligence (AI) model that identifies object interactions and human actions, combined with physics-based simulations is used to accurately evaluate pathogen load distributions and infection risks.

Abstract: A lead device with positioning capabilities can be initiated. The lead device can poll wireless enabled devices within a wireless range. Device identification information associated with the polled wireless enabled devices can be removed. Density can be calculated within the wireless range based on the number of polled wireless enabled devices within the wireless range.