Abstract
AbstractMatching spots between two-dimensional electrophoresis (2-DE) images is a bottleneck in the automation of proteome analysis. Because the matching problem is an NP-hard problem, the solution is usually a heuristic approach or a neural network method. So a Hopfield neural network approach is applied to solve this problem. An energy function is designed to represent the similarity of spots together with its neighbor spots. Experiment showed that Hopfield neural network with appropriate energy function and dynamics could solve the matching problem of spots in 2-DE images.KeywordsEnergy FunctionLeft ImageNeural Network MethodHopfield Neural NetworkSpot MatchThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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