This disclosure provides methods, systems, compositions, and kits for the multiplexed detection of a plurality of analytes in a sample. In some examples, this disclosure provides methods, systems, compositions, and kits wherein multiple analytes may be detected in a single sample volume by acquiring a cumulative measurement or measurements of at least one quantifiable component of a signal. In some cases, additional components of a signal, or additional signals (or components thereof) are also quantified. Each signal or component of a signal may be used to construct a coding scheme which can then be used to determine the presence or absence of any analyte.

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.

The present disclosure describes systems and methods for determining spatial accumulation of signaling molecules within tissue samples. Embodiments of the present disclosure are directed to integrating spatial and expression data for cells in a tissue. Embodiments further describe identifying cell linkages and rendering transcriptome profiles to spatial coordinates. Some embodiments further convolve diffusion information for various ligands and measure effective concentrations within cell areas. Using this information, embodiments are able to predict cell-cell signaling information.

A system and method are disclosed for training a cancer classifier. The method includes, for each training sample comprising a plurality of methylation sequence reads: for each methylation sequence read, applying a probabilistic noise model, corresponding to a genomic region of a plurality of genomics regions that the methylation sequence read overlaps with, to the methylation sequence read to determine an anomaly score indicating a likelihood of observing the methylation pattern in healthy samples. Each probabilistic noise model is trained with methylation sequence reads from healthy samples. The method includes determining a feature vector comprising a feature for each genomic region based on a count of methylation sequence reads overlapping the genomic region with an anomaly score below a threshold anomaly score. The method includes training the cancer classifier with the feature vectors of the training samples to determine a cancer prediction based on an input feature vector.

Methods, systems, apparatuses and computer readable media are provided for characterizing a molecular phenotype of a biological sample using a biological interaction network. A biological interaction network includes a plurality of nodes, each node associated with a corresponding gene or protein. A method includes associating, with each node of the biological interaction network, a corresponding differential abundance value for the gene or protein to which that node corresponds, the differential abundance value derived from a comparison of a representative abundance value for the gene or protein in a biological sample exhibiting the molecular phenotype and a reference abundance value for the gene or protein. The method includes, using the differential abundance values of the nodes of the biological interaction network, performing a hill-climbing algorithm to partition the biological interaction network into clusters. The method includes determining, from the topology of the clusters, a signature of the molecular phenotype.

Methods, systems, apparatuses and computer readable media are provided for characterizing a molecular phenotype of a biological sample using a biological interaction network. A biological interaction network includes a plurality of nodes, each node associated with a corresponding gene or protein. A method includes associating, with each node of the biological interaction network, a corresponding differential abundance value for the gene or protein to which that node corresponds, the differential abundance value derived from a comparison of a representative abundance value for the gene or protein in a biological sample exhibiting the molecular phenotype and a reference abundance value for the gene or protein. The method includes, using the differential abundance values of the nodes of the biological interaction network, performing a hill-climbing algorithm to partition the biological interaction network into clusters. The method includes determining, from the topology of the clusters, a signature of the molecular phenotype.

Disclosed are methods for assessing radiosensitivity based on individual radiation immune sensitivity (iRIS) as a metic to predict radiation response based on its effect on the tumor-immune ecosystem (TIES).

Disclosed are methods for assessing radiosensitivity based on individual radiation immune sensitivity (iRIS) as a metic to predict radiation response based on its effect on the tumor-immune ecosystem (TIES).

A bioinformatics method for determining a risk score that indicates a risk that a subject, in particular a human, will experience a negative clinical event within a certain period of time. The risk score is based on a unique combination of activities of two or more cellular signaling pathways in a subject, wherein the selected cellular signaling pathways are the TGF-β pathway and one or more of a PI3K pathway, a Wnt pathway, an ER pathway, and an HH pathway. The invention includes an apparatus with a digital processor configured to perform such a method, a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such a method, and a computer program comprising program code means for causing a digital processing device to perform such a method. The bioinformatics invention allows for more accurate prognosis of specific negative clinical events in a patient with, for example, a tumor or cancer, such as disease progression, recurrence, development of metastasis, or even death.