Abstract: Disclosed herein include systems, devices, and methods for counterfactual optimization. In some embodiments, spatial omics training data can comprise a plurality of training images each comprising a plurality of molecule channels. A training image label can be generated for each of a plurality of training images indicating presence of at least one T cell in the training image. A plurality of masked training images can be generated from a plurality of training images with any T cell present in a training image of the plurality of training images masked in a masked training image of the plurality of masked training images generated. A model comprising a classifier with the plurality of masked training images as input and the training image label as output can be generated. A counterfactual optimization can be performed to determine a tumor perturbation using a second plurality of images with no T cell present in each of the plurality of images.

Abstract: The present disclosure provides methods, systems, devices, kits, and reagents for performing single cell sequencing (e.g., single cell RNA sequencing) from a low volume, capillary blood (or any low volume blood sample which is not obtained from a vein or by venipuncture).

Abstract: The present disclosure provides methods, systems, devices, kits, and reagents for performing single cell sequencing (e.g., single cell RNA sequencing) from a low volume, capillary blood (or any low volume blood sample which is not obtained from a vein or by venipuncture).

Abstract: Disclosed herein include systems, devices, and methods for flexible machine learning by traversing functionally invariant paths in weight space.

Abstract: Disclosed herein include systems, devices, computer readable media, and methods for resilience determination and damage recovery in neural networks using a weight space and a metric that together form a manifold (such as a pseudo-Riemannian manifold or a Riemannian manifold)

Abstract: The present disclosure provides methods, systems, devices, kits, and reagents for performing single cell sequencing (e.g., single cell RNA sequencing) from a low volume, capillary blood (or any low volume blood sample which is not obtained from a vein or by venipuncture).

Abstract: Disclosed herein include systems, methods, devices, and computer readable media for constructing a neural network by growing and self-organizing.

Abstract: Gene expression vectors of two samples in a gene expression space can be transformed into gene feature vectors in a gene feature space. Gaussian densities and associated weights can be determined for each sample using the corresponding gene feature vectors. Gaussian densities of one samples can be aligned to Gaussian densities of the other sample. A difference between a pair of aligned Gaussian densities of the two samples can be determined, which indicates a difference between subpopulation of cells of the two samples represented by the Gaussian densities of the pair of aligned Gaussian densities.