The present disclosure describes a technology platform for creating and updating records of resources in a ledger. To create a record, a tenant organization may prepare a record to write to the ledger that may be flagged as temporary. Metadata may be added to the record, which flags the record as temporary. The metadata may comprise a unique code and an identification of a user that can approve the temporary record. The unique code and the identification may be sent, by the technology platform, to a device associated with one or more approving devices. Upon receiving the code and the identification of the transaction, the device may sign the unique code and invoke a routine based on the identification. The routine may fetch the temporary record. The device may compare the unique code to a code stored in the metadata of the temporary record. Upon valid verification of the unique code, the device may indicate authorization of the write. Based on the authorization, a proxy node associated with the technology platform may write a definitive record to the ledger based on the temporary record.

Methods described herein include receiving data from flowing a plurality of aptamers over a sample of tumor cells randomly affixed to a surface of a microfluidic device. The tumor cells may include one or more unknown tumor subtypes of cells. The plurality of aptamers may include a plurality of aptamer families. Each aptamer family of the plurality of aptamer families may be determined to bind to at least one possible subtype of the tumor cells. The data may include a measure of binding affinity of each aptamer family to the tumor cells. The method may include analyzing the measure of the binding affinity of each aptamer family to the tumor cells. The analyzing may include classifying the binding affinity. The method may also include determining one or more aptamer families that characterize the one or more unknown tumor subtypes of cells based on the classifying.

The present disclosure provides methods and systems for determining and/or characterizing one or more haplotypes and/or phasing of haplotypes in a nucleic acid sample. In particular, the disclosure provides methods for determining a haplotype and/or phasing of haplotypes in a nucleic acid sample by incorporating synthetic polymorphisms into fragments of a nucleic acid sample and utilizing the synthetic polymorphisms in determining one or more haplotypes and/or phasing of haplotypes.

The present disclosure provides methods and systems for determining and/or characterizing one or more haplotypes and/or phasing of haplotypes in a nucleic acid sample. In particular, the disclosure provides methods for determining a haplotype and/or phasing of haplotypes in a nucleic acid sample by incorporating synthetic polymorphisms into fragments of a nucleic acid sample and utilizing the synthetic polymorphisms in determining one or more haplotypes and/or phasing of haplotypes.

Methods and systems are disclosed in which an automated or semi-automated laboratory may be combined with a machine learning methodology to enable predicting outcomes of chemical reactions or to predict reaction conditions. The model may be trained on reactions including data from the laboratory, purposefully selected to satisfy a desired goal by a user. The user can interact with the process and the model via dedicated user interfaces designed to enable efficient user-machine interaction. The method can be used in the context of multiple challenging problems in chemistry such as steering an automated chemistry laboratory, synthesizing a large collection of compounds such as DNA encoded library, or recommending high yielding reaction conditions for reactions involving drug-like compounds.

Methods and systems are disclosed in which an automated or semi-automated laboratory may be combined with a machine learning methodology to enable predicting outcomes of chemical reactions or to predict reaction conditions. The model may be trained on reactions including data from the laboratory, purposefully selected to satisfy a desired goal by a user. The user can interact with the process and the model via dedicated user interfaces designed to enable efficient user-machine interaction. The method can be used in the context of multiple challenging problems in chemistry such as steering an automated chemistry laboratory, synthesizing a large collection of compounds such as DNA encoded library, or recommending high yielding reaction conditions for reactions involving drug-like compounds.

A method of analysing a structure of a composition of matter in a sample includes obtaining a data set comprising a plurality of spectra from the composition, from a first method of analysis, dividing each of the spectra into a plurality of bins, determining a control parameter or parameters indicative of synchronised fluctuations in signal intensity across some or all channels, resulting in universal correlation between said bins, and determining a partial covariance of different bins across the plurality of spectra using the control parameter to correct the correlation of intensity fluctuations between said bins.

A method of analysing a structure of a composition of matter in a sample includes obtaining a data set comprising a plurality of spectra from the composition, from a first method of analysis, dividing each of the spectra into a plurality of bins, determining a control parameter or parameters indicative of synchronised fluctuations in signal intensity across some or all channels, resulting in universal correlation between said bins, and determining a partial covariance of different bins across the plurality of spectra using the control parameter to correct the correlation of intensity fluctuations between said bins.

An embodiment of the present invention provides a method of creating a classifier indicative of a presence of a medical condition in a subject, comprising receiving chromatogram data indicative of a profile of volatile organic compounds in a sample from each of a first plurality of subjects having the medical condition and a second plurality of subjects without the medical condition, selecting one of the chromatogram data as reference chromatogram data, aligning the remaining chromatogram data in relation to the reference chromatogram data, extracting one or more features from the chromatogram data using a Mexican hat wavelet transform of one or more scales, selecting one or more features of the chromatogram data indicative of the medical condition, and constructing a classifier for determining a boundary between chromatogram data indicative of the medical condition and chromatogram data indicative of an absence of the medical condition.

An embodiment of the present invention provides a method of creating a classifier indicative of a presence of a medical condition in a subject, comprising receiving chromatogram data indicative of a profile of volatile organic compounds in a sample from each of a first plurality of subjects having the medical condition and a second plurality of subjects without the medical condition, selecting one of the chromatogram data as reference chromatogram data, aligning the remaining chromatogram data in relation to the reference chromatogram data, extracting one or more features from the chromatogram data using a Mexican hat wavelet transform of one or more scales, selecting one or more features of the chromatogram data indicative of the medical condition, and constructing a classifier for determining a boundary between chromatogram data indicative of the medical condition and chromatogram data indicative of an absence of the medical condition.