Abstract: The method can include: determining a set of molecules, determining a representation for the set of molecules, and evaluating a property of the set of molecules. In variants, the method can function to evaluate molecules (e.g., pairs of molecules). For example, the method can function to identify high-potential hits (e.g., biologically relevant interactions between molecules) and/or other targets. Additionally or alternatively, the method can function to identify candidate molecules with a target property.

Abstract: The method for processing experimental data can include: determining experimental data (e.g., mass spectrometry spectra) and processing the experimental data. In variants, processing the experimental data can include: identifying one or more molecules, comparing experimental samples, determining a quantification, evaluating a quality of the experimental data, and/or otherwise processing the experimental data. The method can optionally include determining supplemental information, determining a set of candidate molecules, training a model, and/any other suitable steps.

Abstract: The present disclosure relates to a machine-learning computing system for training and running a machine-learning model to estimate peptide-retention time for a sample. The machine-learning model can be configured to process inputs that characterize an individual peptide and/or amino acids in the peptide and to output an estimated retention time within a liquid-chromatography column for the peptide. The machine-learning model can include an encoder-decoder model. The encoder and/or the decoder can include a neural network. A subset of peptides can then be identified that are associated with estimated retention times within a specific elution time period during which portion of the sample was eluted from a chromatography column, and mass-spectrometry data can be analyzed to determine which of the subset of peptides are present within the sample.

Abstract: The present invention relates to proteomics, and techniques for predicting de novo sequencing of chains of amino acids, such as peptides, proteins, or combinations thereof. Particularly, aspects of the present invention are directed to a computer implemented method that includes obtaining a digital representation of a mass spectrum, the digital representation including a plurality of container elements, encoding, using an encoder portion of a bidirectional recurrent neural network of long short term memory cells and gated recurrent unit cells, each container element as an encoded vector, decoding, using a decoder portion of the bidirectional recurrent neural network, each of the encoded vectors into a sequence of amino acids; and recording the sequence of amino acids as a multi-dimensional data set of amino acids types and a probability of each of the amino acid types in each position of the complete amino acid sequence.

Abstract: The method for processing experimental data can include: determining experimental data (e.g., mass spectrometry spectra) and processing the experimental data. In variants, processing the experimental data can include: identifying one or more molecules, comparing experimental samples, determining a quantification, evaluating a quality of the experimental data, and/or otherwise processing the experimental data. The method can optionally include determining supplemental information, determining a set of candidate molecules, training a model, and/any other suitable steps.

Abstract: Example systems and methods for generating virtual immunofluorescence stains for tissue samples are provided. A computing device receives a slide image of a target tissue sample of a particular tissue type. The computing device selects a first trained machine learning (ML) model to generate virtual immunofluorescence (IF) stains of a first type for the particular tissue type based on a user input. The first trained ML model is trained at least based on a first set of stain images of a plurality of training tissue samples with stains of the first type. The computing device generate a virtually stained image of the target tissue sample with the virtual IF stains of the first type using the first trained ML model. The computing device displays the virtually stained image of the target tissue sample with the virtual IF stains of the first type.

Abstract: The method for processing experimental data can include: determining experimental data (e.g., mass spectrometry spectra) and processing the experimental data. In variants, processing the experimental data can include: identifying one or more molecules, comparing experimental samples, determining a quantification, evaluating a quality of the experimental data, and/or otherwise processing the experimental data. The method can optionally include determining supplemental information, determining a set of candidate molecules, training a model, and/any other suitable steps.

Abstract: Methods and systems are disclosed for image process biological samples to infer biomarker values. An image of a biological sample can be accessed. The image can be segmented into a set of patches. Edge detection performed on each patch can be used to identify one or more biological features represented by the patch. An indication of the one or more features of each patch may be used to generate one or more image level metrics. A value of a biomarker may be inferred using the one or more image level metrics. The value of the biomarker can then be output.

Abstract: The present invention relates to proteomics, and techniques for predicting of mass spectrometry data of chains of amino acids, such as peptides, proteins, or combinations thereof. Particularly, aspects of the present invention are directed to a computer implemented method that includes obtaining a digital representation of a peptide sequence, the digital representation including a plurality of container elements, each container element of the plurality of container elements representing an amino acid residue; encoding, using a bidirectional recurrent neural network of long short term memory cells, each container element as an encoded vector; and decoding, using a fully-connected network, each of the encoded vectors into a theoretical output spectrum. The theoretical output spectra are represented as a one-dimensional data set or a multi-dimensional data set including intensity values for each fragment ion including one or more of the amino acid residues in the theoretical output spectra.

Abstract: Methods and systems are disclosed for image process biological samples to infer biomarker values. An image of a biological sample can be accessed. The image can be segmented into a set of patches. Edge detection performed on each patch can be used to identify one or more biological features represented by the patch. An indication of the one or more features of each patch may be used to generate one or more image level metrics. A value of a biomarker may be inferred using the one or more image level metrics. The value of the biomarker can then be output.

Abstract: Methods and systems are disclosed for image process biological samples to infer biomarker values. An image of a biological sample can be accessed. The image can be segmented into a set of patches. Edge detection performed on each patch can be used to identify one or more biological features represented by the patch. An indication of the one or more features of each patch may be used to generate one or more image level metrics. A value of a biomarker may be inferred using the one or more image level metrics. The value of the biomarker can then be output.

Abstract: Methods and systems are disclosed for image process biological samples to infer biomarker values. An image of a biological sample can be accessed. The image can be segmented into a set of patches. Edge detection performed on each patch can be used to identify one or more biological features represented by the patch. An indication of the one or more features of each patch may be used to generate one or more image level metrics. A value of a biomarker may be inferred using the one or more image level metrics. The value of the biomarker can then be output.