# idMotif: An Interactive Motif Identification in Protein Sequences

arXiv: https://arxiv.org/abs/2402.05953 · HTML: https://mlsanity.com/paper/2402.05953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [ferret: a Framework for Benchmarking Explainers on Transformers](https://mlsanity.com/paper/2208.01575/) (2022)
- [Survey on Visual Analysis of Event Sequence Data](https://mlsanity.com/paper/2006.14291/) (2020)
- [Tasks, Techniques, and Tools for Genomic Data Visualization](https://mlsanity.com/paper/1905.02853/) (2019)
- [Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks](https://mlsanity.com/paper/1608.03644/) (2016)
- [Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes](https://mlsanity.com/paper/1907.04112/) (2019)

## Abstract (arXiv)

This article introduces idMotif, a visual analytics framework designed to aid domain experts in the identification of motifs within protein sequences. Motifs, short sequences of amino acids, are critical for understanding the distinct functions of proteins. Identifying these motifs is pivotal for predicting diseases or infections. idMotif employs a deep learning-based method for the categorization of protein sequences, enabling the discovery of potential motif candidates within protein groups through local explanations of deep learning model decisions. It offers multiple interactive views for the analysis of protein clusters or groups and their sequences. A case study, complemented by expert feedback, illustrates idMotif's utility in facilitating the analysis and identification of protein sequences and motifs.