Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a transcriptome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the transcriptome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: Examples include a sheet transport apparatus comprising a sheet inlet facing a specific side of the apparatus. The sheet transport apparatus also comprises a sheet outlet facing the specific side of the apparatus and located below the sheet inlet, the sheet outlet comprising a ceiling between the sheet outlet and the sheet inlet. The sheet transport apparatus further comprises a sheet driving mechanism configured for driving a sheet of media on a media path from the sheet inlet to the sheet outlet, and a flexible and resilient device connected to the ceiling and partially obstructing the sheet outlet.

Abstract: Examples include a sheet transport apparatus comprising a sheet inlet facing a specific side of the apparatus. The sheet transport apparatus also comprises a sheet outlet facing the specific side of the apparatus and located below the sheet inlet, the sheet outlet comprising a ceiling between the sheet outlet and the sheet inlet. The sheet transport apparatus further comprises a sheet driving mechanism configured for driving a sheet of media on a media path from the sheet inlet to the sheet outlet, and a flexible and resilient device connected to the ceiling and partially obstructing the sheet outlet.

Abstract: A method of generating graph data of an object is provided, the object is physical, audio object, text object or color object. The method can include: processing input graph data of at least one object with a graph convolution layer of an edge message passing neural network to obtain vector representations of the node data and edge data of the graph data; processing the vector representations of the edge data and node data with a graph pooling layer of the edge message passing neural network that aggregates the vector representations of the node data and the vector representations of edge data to produce a vector representation of the input graph data; processing the vector representation of the input graph data with a multi-layer perception layer of the edge message passing neural network to generate predicted graph data of a predicted object; and reporting the predicted graph data in a report.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a transcriptome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the transcriptome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a proteome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the proteome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: A method of generating molecular structures includes: providing an ABGM; inputting into the ABGM scored molecules having an objective function value; selecting scored molecules with large objective function values; processing the selected scored molecules through an encoder to obtain latent points; selecting a latent point; sampling neighbor latent points that are within a distance from the selected latent point; processing the sampled neighbor latent points with a decoder to generate generated molecules; and provide a report having at least one generated molecule. The scored molecules can have at least one desired property. The method can include: comparing the generated molecules with selected scored molecules; selecting molecules from the generated molecules that are closest to the selected scored molecules; and providing the selected molecules as candidates for having the at least one property.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a proteome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the proteome signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a biological data signature derived from a tissue or organ of the subject; (b) creating input vectors based on the biological data signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ. The biological data signature can be based on biological pathway activation signatures for genomics, transcriptomics, proteomics, methylomics, metabolomics, lipidomics, glycomics, or secretomics.

Abstract: A graph-to-sequence (G2S) architecture is configured to use graph data of objects to generate sequence data of new objects. The process can be used with objects types that can be represented as graph data and sequence data. For instance, such data is molecular data, where each molecule can be represented as molecular graph and in SMILES. Examples also include popular tasks in deep learning of image-to-text or/and image-to-speech translations. Images can be naturally represented as graphs, while text and speech can be natively represented as sequences. The G2S architecture can include a graph encoder and sample generator that produce latent data in a latent space, which latent data can be conditioned with properties of the object. The latent data is input into a discriminator to obtain real or fake objects, and input into a decoder for generating the sequence data of the new objects.

Abstract: A synthesis protocol for a reaction pathway of a target molecule can be determined by: providing target compound data; performing a chemical synthesis search for at least one reaction pathway for the target compound; processing the target compound data through a single-step reaction enumeration algorithm to obtain at least one reaction step of the least one reaction pathway; processing at least one reaction step with the at least one reaction pathway scoring mechanism model to obtain a reaction step score; constructing reaction pathways based on at least one reaction step and at least one reaction step score; providing a selectivity filter having a selectivity criteria; filtering the reaction pathways so that reactions violating the selectivity criteria is filtered out; ranking the reaction pathways; and providing the reaction pathway ranking.

Abstract: A method for training model to calculate synthetic accessibility includes: accessing molecule database and obtaining molecule; virtually slicing the molecule into fragments; determining a fragment frequency of fragments; calculating molecular descriptors for the fragments; calculating synthetic difficulty score for the molecule; and storing the synthetic difficulty score in a database. A method of evaluating molecular synthetic accessibility includes: selecting target molecule; decomposing the target molecule into molecular fragments; calculating a synthetic difficulty score for the molecular fragments for the target molecule; determining a sum of synthetic difficulty scores for the molecular fragments; determining a fragment density of the molecular fragments; calculating the synthetic accessibility score from the sum of synthetic difficulty scores and fragment densities; and providing the synthetic accessibility score for the target molecule.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Abstract: The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.