Illumina, Inc.
Machine Learning-Based Analysis of Process Indicators to Predict Sample Reevaluation Success

Abstract:

The technology disclosed relates to systems and methods for scoring whether to reevaluate a sample after one or more inconclusive sample evaluation runs. The scores can be based on combination of call rates and readouts from process probes which generate signals indicative of processing parameters at successive stages of sample processing. The system can include a classifier trained to predict whether an additional sample evaluation run will produce conclusive results for the sample. The system can use a plurality of readouts from probes grouped into sample-dependent and sample-independent process probes. The system can use a plurality of readouts of radiant signals from probes grouped according to three stages of the sample evaluation run.