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: 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.