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In this article, a one-dimensional self-organizing feature map (SOFM) neural network integrated with semi-supervised learning is used to predict the class label of gene expression data under the scarcity of the labeled patterns. Iterative learning of the semi-supervised SOFM network is carried out using a few labeled patterns along with some selected unlabeled patterns. The unlabeled patterns, for which the maximum target value is greater than a threshold, are selected as the confident ones. Results are found to be encouraging.