Systems and methods of discovering compounds with biological properties are provided. A first training dataset is obtained, including chemical structures and biological properties. Projections of compounds are obtained by projecting chemical structure information into a latent representation space using encoder weights. Compounds are classified by inputting projections into the classifier using classifier weights. The encoder and classifier are trained by comparing the classification of each compound to actual biological properties and updating the respective weights. A second training dataset is obtained including chemical structures. Projections of compounds are obtained by projecting chemical structure information into a latent representation space using encoder weights. Chemical structures are obtained by inputting projections into a decoder using decoder weights. The decoder is trained by comparing outputted and actual chemical structures and updating the respective weights. Candidate compounds not present in the first and second datasets are identified using the trained encoder, classifier, and decoder.

Systems and methods of discovering compounds with biological properties are provided. A first training dataset is obtained, including chemical structures and biological properties. Projections of compounds are obtained by projecting chemical structure information into a latent representation space using encoder weights. Compounds are classified by inputting projections into the classifier using classifier weights. The encoder and classifier are trained by comparing the classification of each compound to actual biological properties and updating the respective weights. A second training dataset is obtained including chemical structures. Projections of compounds are obtained by projecting chemical structure information into a latent representation space using encoder weights. Chemical structures are obtained by inputting projections into a decoder using decoder weights. The decoder is trained by comparing outputted and actual chemical structures and updating the respective weights. Candidate compounds not present in the first and second datasets are identified using the trained encoder, classifier, and decoder.

A method for predicting the cell spatial relation based on single-cell transcriptome sequencing data includes the steps of obtaining a probability matrix P of a cell-cell interaction strength matrix A based on single-cell transcriptome sequencing data; reconstructing, according to the obtained probability matrix P of the cell-cell interaction strength matrix A, a three-dimensional spatial structure in which cells interact with each other; and for each cell in the reconstructed three-dimensional spatial structure in which cells interact with each other, determining the intercellular distance threshold for each cell to interact with h cells on average to obtain an intercellular interaction network. The method requires only the single-cell transcriptome sequencing data to predict the interaction of the cells in three-dimensional space, which breaks the limitation of the existing technology that needs to obtain the spatial relationship of cells through imaging.

A method for predicting the cell spatial relation based on single-cell transcriptome sequencing data includes the steps of obtaining a probability matrix P of a cell-cell interaction strength matrix A based on single-cell transcriptome sequencing data; reconstructing, according to the obtained probability matrix P of the cell-cell interaction strength matrix A, a three-dimensional spatial structure in which cells interact with each other; and for each cell in the reconstructed three-dimensional spatial structure in which cells interact with each other, determining the intercellular distance threshold for each cell to interact with h cells on average to obtain an intercellular interaction network. The method requires only the single-cell transcriptome sequencing data to predict the interaction of the cells in three-dimensional space, which breaks the limitation of the existing technology that needs to obtain the spatial relationship of cells through imaging.

Methods for generating a composite biomarker that identifies a predicted level of responsiveness of a subject to a particular type of an immunotherapy treatment is provided. The method can include generating genomic metrics that represent one or more characteristics corresponding to one or more DNA sequences. The method can also include generating transcriptomic metrics represent one or more characteristics corresponding to a set of peptides that are translated from a corresponding RNA sequence of the one or more RNA sequences. The method can also include generating a composite biomarker score derived from the set of genomic metrics and the set of transcriptomic metrics. The method can also include determining, based on the composite biomarker score, a predicted level of responsiveness of the subject to a particular type of an immunotherapy treatment.

Methods for generating a composite biomarker that identifies a predicted level of responsiveness of a subject to a particular type of an immunotherapy treatment is provided. The method can include generating genomic metrics that represent one or more characteristics corresponding to one or more DNA sequences. The method can also include generating transcriptomic metrics represent one or more characteristics corresponding to a set of peptides that are translated from a corresponding RNA sequence of the one or more RNA sequences. The method can also include generating a composite biomarker score derived from the set of genomic metrics and the set of transcriptomic metrics. The method can also include determining, based on the composite biomarker score, a predicted level of responsiveness of the subject to a particular type of an immunotherapy treatment.

A platform providing methods and systems for prevention and/or treatment of a health condition, where a method can include: simultaneously reducing severity symptoms of the health condition and comorbid conditions upon: receiving a set of samples from one or more subjects; receiving a biometric dataset from one or more subjects; receiving a lifestyle dataset from one or more subjects; returning a genomic single nucleotide polymorphism (SNP) profile and a baseline microbiome state upon processing the set of samples, the biometric dataset, and the lifestyle dataset with a set of transformation operations; generating personalized intervention plans for the one or more subjects upon processing the genomic SNP profile and the baseline microbiome state with a multi-omic model; and executing the personalized intervention plans for the one or more subjects.

A system for updating a descriptor trail using artificial intelligence. The system is configured to display on a graphical user interface operating on a processor connected to a memory an element of diagnostic data. The system is configured to receive from a user client device an element of user constitutional data. The system is configured to display on a graphical user interface the element of user constitutional data. The system is configured to prompt an advisor input on a graphical user interface. The system is configured to receive from an advisor client device an advisor input containing an element of advisory data. The system is configured to generate an updated descriptor trail as a function of the advisor input. The system is configured to display the updated descriptor trail on a graphical user interface.

A system for updating a descriptor trail using artificial intelligence. The system is configured to display on a graphical user interface operating on a processor connected to a memory an element of diagnostic data. The system is configured to receive from a user client device an element of user constitutional data. The system is configured to display on a graphical user interface the element of user constitutional data. The system is configured to prompt an advisor input on a graphical user interface. The system is configured to receive from an advisor client device an advisor input containing an element of advisory data. The system is configured to generate an updated descriptor trail as a function of the advisor input. The system is configured to display the updated descriptor trail on a graphical user interface.

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.