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After its introduction in 1982, the Hopfield model has been extensivelyapplied for classification and pattern recognition. Recently, its greatpotential in gene expression patterns retrieval has also been shown. Followingthis line, we develop Hope4Genes a single-sample class prediction algorithmbased on a Hopfield-like model. Differently from previous works, we here testedthe performances of the algorithm for class prediction, a task of fundamentalimportance for precision medicine and therapeutic decision-making. Hope4Genesproved better performances than the state-of-art methodologies in the fieldindependently of the size of the input dataset, its profiling platform, thenumber of classes and the typical class-imbalance present in biological data.Our results provide encoraging evidence that the Hopfield model, together withthe use of its energy for the estimation of the false discoveries, is aparticularly promising tool for precision medicine.

Hope4Genes: a Hopfield-like class prediction algorithm for transcriptomic data