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

## TL;DR

Hope4Genes is a novel Hopfield-like algorithm for single-sample transcriptomic classification, outperforming existing methods and showing promise for precision medicine applications.

## Contribution

It introduces Hope4Genes, a new Hopfield-based classifier that improves accuracy in gene expression data classification regardless of dataset size or class imbalance.

## Key findings

- Outperforms state-of-the-art classification methods
- Effective across various dataset sizes and platforms
- Provides a means to estimate false discoveries using energy measures

## 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.