IDENTIFICATION AND CLASSIFICATION OF AUTOANTIBODY REPERTOIRES (WESTERN BLOTS) WITH A PATTERN-RECOGNITION ALGORITHM BY AN ARTIFICIAL NEURAL-NETWORK

The screening of sera for autoantibodies with Western blots reveals co mplex repertoires. the compostion of such repertoires depends on genet ic control of autoantibody-producing cells, the individual's history o f exposure to its own and to foreign antigens, and also on the presenc e of autoimmune diseases. Our method shows how staining patterns of We stern blots can be recoded as binary or grey-value vectors. Vectors ar e transferred to artificial neural networks for learning. Artificial n eural networks are able to recognize group-specific antibody binding p atterns. Staining patterns can be attributed to diagnostic groups. Thi s may support diagnostic procedures.