Abstract: Embodiments of the invention are disclosed that implement deep learning methods using artificial neural networks to provide improved automated predictions regarding whether a particular sample comprises a target molecule. In some embodiments, a convolutional neural network is used. In some embodiments, an artificial neural network is trained using a class-weighted error determination. These and other embodiments are disclosed herein.

Abstract: Embodiments of the invention are disclosed that provide improved computer systems, computerized methods, and computer program products for generating and evaluating automated predictions regarding whether a particular amplification curve from a qPCR assay indicates presence of a target molecule in a sample. In some embodiments, predictions are generated using deep learning networks. In some embodiments, curve-quality predictions are generated and used to assess whether an amplification prediction can be reliably made from a particular amplification curve or whether the curve reflects an anomaly in the qPCR assay. In various embodiments, prediction confidence data is also generated and used, along with prediction data, in an electronic user interface to improve qPCR measurement.

Abstract: The present disclosure provides systems, methods, and a computer-readable storage medium for identifying and selecting highly relevant sets of genes (or Gene Signatures and biomarkers), which, when coupled with clinical and/or demographic data are highly predictive of potential clinical responses to a targeted therapy. It also provides systems, methods, and a computer-readable storage medium for determining the expected response to the targeted therapy when using the selected gene/biomarker set.

Abstract: Embodiments of the invention are disclosed that implement deep learning methods using artificial neural networks to provide improved automated predictions regarding whether a particular sample comprises a target molecule. In some embodiments, a convolutional neural network is used. In some embodiments, an artificial neural network is trained using a class-weighted error determination. These and other embodiments are disclosed herein.

Abstract: A biological analysis system and an associated method are provided. The method is used for recovering off scale data in an image produced by a camera in a capillary electrophoresis instrument. The method comprises the steps of identifying bins of the image where electron counts exceed a maximum number of counts; setting an off-scale flag for the identified bins; and processing the image to obtain a recovered dye signal, based on the flag set for each bin, and using a dye matrix.

Abstract: A biological analysis system and an associated method are provided. The method is used for recovering off scale data in an image produced by a camera in a capillary electrophoresis instrument. The method comprises the steps of identifying bins of the image where electron counts exceed a maximum number of counts; setting an off-scale flag for the identified bins; and processing the image to obtain a recovered dye signal, based on the flag set for each bin, and using a dye matrix.

Abstract: Embodiments implementing selected automated quality control operations in sample processing instruments that analyze dye-labeled samples are disclosed. In some embodiments, temperature and/or pressure parameters are measured and compared to thresholds to determine whether warning should be provided and/or actions taken. Embodiments for implementing automated correction of spectral error during the instrument's normal runtime operation without requiring the user to conduct a special, separate calibration run are also disclosed.

Abstract: The present disclosure provides systems, methods, and a computer-readable storage medium for identifying and selecting highly relevant sets of genes (or Gene Signatures and biomarkers), which, when coupled with clinical and/or demographic data are highly predictive of potential clinical responses to a targeted therapy. It also provides systems, methods, and a computer-readable storage medium for determining the expected response to the targeted therapy when using the selected gene/biomarker set.