ArticleGenome biology2021
Biologically relevant transfer learning improves transcription factor binding prediction.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- TPCAV: Interpreting deep learning genomics models via concept attribution.bioRxiv : the preprint server for biology · 2026Article
- Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts.Genome biology · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution.Briefings in bioinformatics · 2025Article
- Predicting gene expression from DNA sequence using deep learning models.Nature reviews. Genetics · 2025Review
- Evaluation of Small-Molecule Binding Site Prediction Methods on Membrane-Embedded Protein Interfaces.Journal of chemical information and modeling · 2025Article
- Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.Cell genomics · 2025Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction.NAR genomics and bioinformatics · 2024Article
- TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.Genome biology · 2024Article
- TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.Plant communications · 2024Article
- Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.bioRxiv : the preprint server for biology · 2024Article
- Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing.Nature communications · 2024Article
- Article
- Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.BMC genomics · 2023Article
- ExplaiNN: interpretable and transparent neural networks for genomics.Genome biology · 2023Article
- Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility.NAR genomics and bioinformatics · 2023Article
- Decoding enhancer complexity with machine learning and high-throughput discovery.Genome biology · 2023Review
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundDeep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall model performance, compared to training a separate model for each new task.
resultsWe assess a transfer learning strategy for TF binding prediction consisting of a pre-training step, wherein we train a multi-task model with multiple TFs, and a fine-tuning step, wherein we initialize single-task models for individual TFs with the weights learned by the multi-task model, after which the single-task models are trained at a lower learning rate. We corroborate that transfer learning improves model performance, especially if in the pre-training step the multi-task model is trained with biologically relevant TFs. We show the effectiveness of transfer learning for TFs with ~ 500 ChIP-seq peak regions. Using model interpretation techniques, we demonstrate that the features learned in the pre-training step are refined in the fine-tuning step to resemble the binding motif of the target TF (i.e., the recipient of transfer learning in the fine-tuning step). Moreover, pre-training with biologically relevant TFs allows single-task models in the fine-tuning step to learn useful features other than the motif of the target TF.
conclusionsOur results confirm that transfer learning is a powerful technique for TF binding prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.