# Memory Matching Networks for Genomic Sequence Classification

arXiv: https://arxiv.org/abs/1702.06760 · HTML: https://mlsanity.com/paper/1702.06760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 1 of 1)

- [GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing](https://mlsanity.com/paper/2501.06828/) (2025)

## Abstract (arXiv)

When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning over inputs and supporting sets. In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites. Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or nonbinding site.