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.

Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.

Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

A user may select one or more potential common ancestors with a DNA match to view the target individual's relationship with them. The process may include identifying, from a first genealogical profile of the target individual. A first individual has a first linkage that connects the target individual towards the selected potential common ancestor. The process may also include identifying, from a second genealogical profile of the DNA match, a second individual who has a second linkage that connects the DNA match towards the selected potential common ancestor. The process may further include connecting the first linkage and the second linkage with the selected potential common ancestor by adding one or more individuals whose profiles are retrieved from other searchable genealogical profiles stored in the online system. With the nodes and connections available, the process may generate a map of visual connections between the target individual and the DNA match.

Disclosed is a method of qualifying a subgroup of target binding biomolecules from a larger group of target binding biomolecules for analysis. A competitive immunoassay including a target protein is used to identify 100 interactions between different pairs of the target binding biomolecules and interaction profiles are generated 200. Each target binding biomolecule is allocated 300 to a bin representing an epitope family and identified bins are associated in a circular or semi-circular bin chart on a display with identified respective target binding biomolecule(s). Based on the association 400 between identified bins and identified respective target binding molecule(s) in the bin chart, a subgroup of target binding biomolecules is selected 500 for further analysis by selecting one or more of the target binding biomolecule(s) of one or more of the bins.

Disclosed is a method of qualifying a subgroup of target binding biomolecules from a larger group of target binding biomolecules for analysis. A competitive immunoassay including a target protein is used to identify 100 interactions between different pairs of the target binding biomolecules and interaction profiles are generated 200. Each target binding biomolecule is allocated 300 to a bin representing an epitope family and identified bins are associated in a circular or semi-circular bin chart on a display with identified respective target binding biomolecule(s). Based on the association 400 between identified bins and identified respective target binding molecule(s) in the bin chart, a subgroup of target binding biomolecules is selected 500 for further analysis by selecting one or more of the target binding biomolecule(s) of one or more of the bins.

A computer-implemented method for generating a data visualization for operating a digital polymerase chain reaction (dPCR) system comprises, the method comprising receiving fluorescent emission data comprising a plurality of data points corresponding to fluorescence emission from a plurality of reaction sites of a substrate, the fluorescent emission data indicative of presence or absence of amplification product of a dPCR. The method further comprises displaying a data visualization of the plurality of data points in a spatial representation of the substrate such that each data point is displayed at a relative spatial location of its corresponding reaction site of the plurality of reaction sites of the substrate, a first set of data points being displayed with a first indication based on meeting a first quality value threshold and second set of data points being displayed with a second indication, differing from the first indication, based on a second quality value threshold.

A computer-implemented method for generating a data visualization for operating a digital polymerase chain reaction (dPCR) system comprises, the method comprising receiving fluorescent emission data comprising a plurality of data points corresponding to fluorescence emission from a plurality of reaction sites of a substrate, the fluorescent emission data indicative of presence or absence of amplification product of a dPCR. The method further comprises displaying a data visualization of the plurality of data points in a spatial representation of the substrate such that each data point is displayed at a relative spatial location of its corresponding reaction site of the plurality of reaction sites of the substrate, a first set of data points being displayed with a first indication based on meeting a first quality value threshold and second set of data points being displayed with a second indication, differing from the first indication, based on a second quality value threshold.