Using machine learning to optimize assays for single-cell targeted DNA sequencing

The disclosure generally relates to using machine learning to optimize assays for single cell targeted DNA sequencing. In an exemplary embodiment, amplicons are designed for disease detection assays. An exemplary amplicon design step includes the steps of (1) receiving empirical data of a plurality of initial attributes from a panel of primary amplicons sequenced with target molecules, each of the initial attributes defining at least one performance criteria for a respective amplicon; (2) ranking performance of each amplicon according to a predefined criteria; (3) from among the ranked amplicons, (i) selecting a plurality of key attributes, and (ii) selecting one or more substantially independent and non-correlating attributes, to form a group of selected primary amplicon attributes; (4) calculate a plurality of statistical parameters for each of the selected primary amplicon attributes; and (5) configure a plurality of secondary amplicons wherein the secondary amplicons include secondary amplicon parameters consistent with the statistical parameters of the selected primary amplicons.