Predicting behavior of an enzyme-linked immunoassay model by using commercially available neural network software.

Setting up new immunoassays can be a laborious and expensive task. A relatively new form of multivariate analysis known as neural networks can be applied to this problem with potential savings in reagents and technician time. Neural network software programs for personal computers are now available. We applied one such software package (Brainmaker) to a model ELISA system for measuring human serum albumin. Random combinations of four variable ELISA conditions (antigen concentration, primary and secondary antibody titers, and time for chromagen development) were used to train a three-layered feed-forward network. The trained network was then used to predict measured absorbances as a function of the four input variables in separate cross-validation sets. The network adequately predicted the effect of the input variables on the absorbance produced. With use of such methods, optimal conditions for the linear dependence of absorbance on antigen concentration can be evaluated on the computer rather than in the laboratory, with subsequent savings of time and money.