# Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5

arXiv: https://arxiv.org/abs/1811.00416 · HTML: https://mlsanity.com/paper/1811.00416/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Genomic Language Models: Opportunities and Challenges](https://mlsanity.com/paper/2407.11435/) (2024)
- [Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2](https://mlsanity.com/paper/2604.21690/) (2026)
- [Neural Networks beyond explainability: Selective inference for sequence motifs](https://mlsanity.com/paper/2212.12542/) (2022)
- [BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data](https://mlsanity.com/paper/2511.15330/) (2025)
- [Validating Interpretability in siRNA Efficacy Prediction: A Perturbation-Based, Dataset-Aware Protocol](https://mlsanity.com/paper/2602.10152/) (2026)
- [A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization](https://mlsanity.com/paper/2603.12073/) (2026)
- [EvoLen: Evolution-Guided Tokenization for DNA Language Model](https://mlsanity.com/paper/2604.08698/) (2026)

## Abstract (arXiv)

TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence data. This technical note focuses on version v0.5.6.5. The implementation is available at https://github.com/kundajelab/tfmodisco/tree/v0.5.6.5