ArticleScientific reports2019
Hope4Genes: a Hopfield-like class prediction algorithm for transcriptomic data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Article
- Quantum Computing Approaches for Vector Quantization-Current Perspectives and Developments.Entropy (Basel, Switzerland) · 2023Article
- Article
- Article
- Hardware-Based Hopfield Neuromorphic Computing for Fall Detection.Sensors (Basel, Switzerland) · 2020Article
- Article
- Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.Frontiers in genetics · 2020Article
- Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
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2 authors.
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Abstract
After its introduction in 1982, the Hopfield model has been extensively applied for classification and pattern recognition. Recently, its great potential in gene expression patterns retrieval has also been shown. Following this line, we develop Hope4Genes a single-sample class prediction algorithm based on a Hopfield-like model. Differently from previous works, we here tested the performances of the algorithm for class prediction, a task of fundamental importance for precision medicine and therapeutic decision-making. Hope4Genes proved better performances than the state-of-art methodologies in the field independently of the size of the input dataset, its profiling platform, the number of classes and the typical class-imbalance present in biological data. Our results provide encoraging evidence that the Hopfield model, together with the use of its energy for the estimation of the false discoveries, is a particularly promising tool for precision medicine.
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