A memory device includes an array of memory cells configured on a die or chip and coupled to sense lines and access lines of the die or chip and a respective sense amplifier configured on the die or chip coupled to each of the sense lines. Each of a plurality of subsets of the sense lines is coupled to a respective local input/output (I/O) line on the die or chip for communication of data on the die or chip and a respective transceiver associated with the respective local I/O line, the respective transceiver configured to enable communication of the data to one or more device off the die or chip.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

The present disclosure provides an entity relation mining method based on a biomedical literature, including the following steps: querying a disease-associated biomedical literature in a public database, and performing data preprocessing to obtain biomedical text data; performing biomedical named entity recognition on obtained biomedical text data in combination with a regex matching pattern and a deep learning model; and mining an entity relation with transfer learning and reinforcement learning based on an entity recognition result. By acquiring the disease-associated biomedical literature from a network, extracting an abstract and a title and performing entity recognition and relation mining, the present disclosure can effectively recognize biomedical noun entities in the literature and mine potential relations between various entities.

A method (100) for standardizing gene nomenclature, comprising: (i) receiving (110) a source; (ii) tokenizing (120) the source; (iii) comparing (130) a first token to a prefix tree structure with a root node, edges, and leaf nodes; (iv) determining (140) which edge extending from the root node to associated first leaf nodes the first token matches; (v) updating (150) an identification pointer with the location of the first leaf node; (vi) determining (160) which of one or more edges that a second token matches; (vii) updating (170) the identification pointer with the location of the second leaf node; (viii) repeating (172) the determining (160) and updating (170) steps with subsequent tokens until a subsequent token fails to match an edge extending from a leaf node or there is no edge extending from the leaf node; and (ix) providing (180) an identification of a canonical gene name.

A method (100) for standardizing gene nomenclature, comprising: (i) receiving (110) a source; (ii) tokenizing (120) the source; (iii) comparing (130) a first token to a prefix tree structure with a root node, edges, and leaf nodes; (iv) determining (140) which edge extending from the root node to associated first leaf nodes the first token matches; (v) updating (150) an identification pointer with the location of the first leaf node; (vi) determining (160) which of one or more edges that a second token matches; (vii) updating (170) the identification pointer with the location of the second leaf node; (viii) repeating (172) the determining (160) and updating (170) steps with subsequent tokens until a subsequent token fails to match an edge extending from a leaf node or there is no edge extending from the leaf node; and (ix) providing (180) an identification of a canonical gene name.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

A method and a device are for exchanging information regarding the clinical implications genomic variations. In an embodiment, the method includes receiving login-data of a user; evaluating the login-data received; establishing an encrypted data connection to the user after the evaluating indicates a positive evaluation of the login-data; saving, upon receiving a dataset in a context of a genomic variation, the dataset received in a memory, context-related with the genomic variation; and evaluating, upon a user request being received and connected with a search query for the genomic variation, a set of datasets from the memory, the datasets being context-related with the genomic variation and the set including the datasets that the user is authorized to receive, and sending the set of datasets to the user.

Material in a supply chain is tracked by a method of applying a DNA taggant set to a first batch of the material produced by a first supplier of the material. The DNA taggant set corresponds to a tag string corresponding to the first supplier. The first batch is aggregated with a second batch to create an aggregated lot. A sample is selected from the aggregated lot and tested to determine a DNA taggant set of the sample. After selecting a sample from the aggregated lot, the sample may be labeled with a grade and then placed in a receptacle corresponding to the grade.