Title :
A hybrid system approach to multivariate analysis of flow cytometry data
Author :
Fu, LiMin ; Yang, Mark ; Braylan, Raul ; Benson, Neal
Author_Institution :
Florida Univ., Gainesville, FL, USA
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 determinacy
Keywords :
biological techniques and instruments; blood; cellular transport and dynamics; data analysis; learning (artificial intelligence); medical computing; neural nets; adaptive data analysis scheme; antigenic properties; blood samples; connection weights; connectionist network; determinacy; flow cytometry data; knowledge decoding; learning equation; multivariate analysis; problem solving structure; self-consistency; statistical validation; symbolic form; Blood; Cells (biology); Data analysis; Decoding; Fluorescence; Instruments; Light scattering; Neural networks; Particle measurements; Pattern analysis;
Conference_Titel :
Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on
Conference_Location :
Durham, NC
Print_ISBN :
0-8186-2742-5
DOI :
10.1109/CBMS.1992.244933
