A hybrid system approach to multivariate analysis of flow cytometry data

Abstract

In dealing with massive flow cytometric data, an adaptive data analysis scheme has been developed. The problem solving structure is configured as a connectionist network. Information is encoded in the form of connection weights. The structure evolves as more data are seen by adjusting its weights, governed by a learning equation. The knowledge embedded in the network can be further decoded in symbolic form. The results are reported from the domain of measuring the antigenic properties of blood samples. The technique has been validated statistically with respect to its self-consistency and deterministicity.