mlsanityData to 14 Oct 2026

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

arXiv 1811.00416 ↗First posted 31 Oct 2018cs.LG q-bio.GN stat.MLcited by 7

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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

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

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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 4.

7 papers.

  1. 2024Genomic Language Models: Opportunities and Challengescited by 20
  2. 2026Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2cited by 1
  3. 2022Neural Networks beyond explainability: Selective inference for sequence motifscited by 0
  4. 2025BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Datacited by 0
  5. 2026Validating Interpretability in siRNA Efficacy Prediction: A Perturbation-Based, Dataset-Aware Protocolcited by 0
  6. 2026A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterizationcited by 0
  7. 2026EvoLen: Evolution-Guided Tokenization for DNA Language Modelcited by 0