Disclosed is a measurement method for measuring a test substance contained in a biological sample based on a predetermined measurement principle, comprising acquiring a first measured value of the test substance using a first measurement reagent, and operating the first measured value to an arithmetic value when measured using a second measurement reagent different from the first measurement reagent, by using arithmetic information designed to make a first cut-off value for the measured value obtained using the first measurement reagent correspond to a second cut-off value for a measured value obtained using the second measurement reagent.

Disclosed is a measurement method for measuring a test substance contained in a biological sample based on a predetermined measurement principle, comprising acquiring a first measured value of the test substance using a first measurement reagent, and operating the first measured value to an arithmetic value when measured using a second measurement reagent different from the first measurement reagent, by using arithmetic information designed to make a first cut-off value for the measured value obtained using the first measurement reagent correspond to a second cut-off value for a measured value obtained using the second measurement reagent.

The invention relates to an in silico screening method to identify candidate excipients for reducing aggregation of a protein in a formulation. The method combines computational molecular modeling and molecular dynamics simulations to identify sites on a protein where non-specific self-interaction and interaction of different test excipients may occur, determine the relative binding energies of such interactions, and select one or more test excipients that meet specified interaction criteria for use as candidate excipients in empirical screening studies.

The invention relates to an in silico screening method to identify candidate excipients for reducing aggregation of a protein in a formulation. The method combines computational molecular modeling and molecular dynamics simulations to identify sites on a protein where non-specific self-interaction and interaction of different test excipients may occur, determine the relative binding energies of such interactions, and select one or more test excipients that meet specified interaction criteria for use as candidate excipients in empirical screening studies.

A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

The present disclosure belongs to the technical field of intelligent kin recognition, and relates to a new biological kin recognition method and system based on unsupervised clustering of mRNA bases, including the following steps: step S1, extracting base codons from an mRNA chain, and re-encoding the base codons according to encoding rules; step S2, converting a re-encoded base chain into a document capable of being identified by a model; step S3, inputting the document into the model to vectorize base texts, and clustering vectorized base texts; and step S4, visualizing clustering results to obtain a biological kin recognition result. The present disclosure does not need to artificially annotate the data, saves labor costs, and avoids effects of artificial factors on taxonomical results, featuring simple use, efficient program run, and fast speed.

A system and method for optimizing the latent space in generative machine learning models, and applications of the optimizations for use in the de novo generation of molecules for both ligand-based and pocket-based generation. The ligand-based optimizations comprise a tunable reward system based on a multi-property model and further define new measurable metrics: molecular novelty and uniqueness. The pocket-based optimizations comprise an initial multi-property optimization followed up by either a seed-based optimization or a relaxed-based optimization.