As shown in Figure 1, three separate convolutions Conv 4,1, Conv 4,2 and Conv 4,3 are applied to each input channel of the feature matrix M2

Multi-Scale Convolution with Attention Module: This module utilises multi-scale convolutions with attention to further enhance the local features captured by the feature extraction module · 2015

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TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites

cs.LG · 2025-02-03 · conditional · novelty 4.0

TFBS-Finder reports accuracy 0.930, PR-AUC 0.961 and ROC-AUC 0.961 on 165 ENCODE TFBS datasets, outperforming BERT-TFBS and other baselines.

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TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites cs.LG · 2025-02-03 · conditional · none · ref 4
TFBS-Finder reports accuracy 0.930, PR-AUC 0.961 and ROC-AUC 0.961 on 165 ENCODE TFBS datasets, outperforming BERT-TFBS and other baselines.

As shown in Figure 1, three separate convolutions Conv 4,1, Conv 4,2 and Conv 4,3 are applied to each input channel of the feature matrix M2

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