Abstract: Some techniques relate to projecting aptamer representations into an embedding space and clustering the representations. A cluster-specific binding metric can be defined for each cluster based on aptamer-specific binding metrics of aptamers associated with the cluster. A subset of the clusters can be selected based on the cluster-specific binding metrics. Identifications of aptamers assigned to the subset of clusters can then be output.

Abstract: Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for machine learning classification based on separate processing of multiple views. In some implementations, a system obtains image data for multiple images showing different views of an object. A machine learning model is used to generate a separate output based on each the multiple images individually. The outputs for the respective images are combined to generate a combined output. A predicted characteristic of the object is determined based on the combined output. An indication of the predicted characteristic of the object is provided.

Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind a target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data, identifying, by a first machine-learning model having model parameters learned from the initial sequence data, a first set of aptamer sequences, obtaining, using an in vitro binding selection process, subsequent sequence data including sequences from the first set of aptamer sequences, identifying, by a second machine-learning model having model parameters learned from the subsequent sequence data, a second set of aptamer sequences, determining, using one or more in vitro assays, analytical data for aptamers synthesized from the second set of aptamer sequences, and identifying a final set of aptamer sequences from the second set of aptamer sequences based on the analytical data associated with each aptamer.

Abstract: A latent space is defined to represent sequences using training data and a machine-learning model. The training data identifies sequences of molecules and binding-approximation metrics that characterizes whether the molecules bind to a particular target and/or that approximate an extent to which the molecule is more likely to bind to the particular target than some other molecules. Supplemental training data is accessed that identifies other sequences of other molecules and binding affinity scores quantifying binding strengths between the molecules and the particular target. Projections of representations of the other sequences in the supplemental training data are projected in the latent space using the binding affinity scores. An area or position of interest within the latent space is identified based on the projections. A particular sequence represented within or at the area or position of interest or at the position of interest is identified for downstream processing.

Abstract: Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.

Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data for aptamers that bind to a target, measuring a first signal to noise ratio within the initial sequence data, provisioning, based on the first signal to noise ratio, a first machine-learning system, generating, by the first machine-learning system, a first set of aptamer sequences, obtaining subsequent sequence data for aptamers that bind to the target, measuring a second signal to noise ratio within the subsequent sequence data, provisioning, based on the second signal to noise ratio, a second machine-learning system, generating, by the second machine-learning system, a second set of aptamer sequences, and outputting the second set of aptamer sequences.

Abstract: The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining sequence data for aptamers that bind to a target, where the sequence data has a first signal to noise ratio, generating, by a search process, a first set of aptamer sequences derived from the sequence data, obtaining subsequent sequence data for subsequent aptamers that bind to the target, where the subsequent aptamers includes aptamers synthesized from the first set of aptamer sequences, and the subsequent sequence data has a second signal to noise ratio greater than the first signal to noise ratio, generating, by a linear machine-learning model, a second set of aptamer sequences derived from the subsequent sequence data, and outputting the second set of aptamer sequences.

Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

Abstract: Systems and methods for managing chemical recycling processes include accessing characterization data of a feedstock, the characterization data comprising one or more spectra collected according to one or more spectroscopic methods. The methods include predicting, using the characterization data, a set of constituent materials included in the feedstock. The methods include predicting a material composition of the feedstock using the predicted set of constituent materials. The methods include identifying, at least in part using the predicted material composition of the feedstock, one or more target products. The methods include generating a set of chemical reaction schemas enabling a conversion of at least part of the feedstock into the one or more target products. The methods also include storing identifications of the material composition of the feedstock, the one or more target products, and the set of chemical reaction schemas in a data store.

Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

Abstract: Embodiments in the present disclosure include systems and methods related to a high-availability architecture for a cloud-based concurrent-access collaboration platform. The disclosed technology relates to an active data center which includes multiple document server instances that handle user requests for concurrently accessing documents. Multiple document server instances are implemented on a single physical server. This architecture uses an instance assignment manager to assign documents to the document server instances, a primary repository to store backup snapshots of the documents, and a datastore to store all changes made to the documents. The disclosed technology also involves a backup data center that can be swapped with the active data center automatically.

Abstract: Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.

Abstract: Embodiments in the present disclosure include systems and methods related to a high-availability architecture for a cloud-based concurrent-access collaboration platform. The disclosed technology relates to an active data center which includes multiple document server instances that handle user requests for concurrently accessing documents. Multiple document server instances are implemented on a single physical server. This architecture uses an instance assignment manager to assign documents to the document server instances, a primary repository to store backup snapshots of the documents, and a datastore to store all changes made to the documents. The disclosed technology also involves a backup data center that can be swapped with the active data center automatically.