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 complex repertoires. the compostion of such repertoires depends on genetic control of autoantibody-producing cells, the individual's history of exposure to its own and to foreign antigens, and also on the presence of autoimmune diseases. Our method shows how staining patterns of Western blots can be recoded as binary or grey-value vectors. Vectors are transferred to artificial neural networks for learning. Artificial neural networks are able to recognize group-specific antibody binding patterns. Staining patterns can be attributed to diagnostic groups. This may support diagnostic procedures.