DeepCTF – AI-Powered TFBS Prediction

Enhancing gene regulation analysis with deep learning and DNA shape integration.

Problem Statement: Traditional TFBS prediction models struggle with feature extraction, long-range dependencies, and integrating DNA shape information. DeepCTF addresses these limitations by leveraging deep learning techniques.

DeepCTF AI Model

Milestones Achieved

High-Accuracy TFBS Prediction
DeepCTF outperforms traditional models with state-of-the-art performance.
Hybrid Deep Learning Model
Combines CNN, BiLSTM, and self-attention for comprehensive sequence analysis.
DNA Shape Feature Integration
Uses Minor Groove Width (MGW), Roll, Propeller Twist (ProT), and Helix Twist (HelT) for better accuracy.
Attention Mechanism for Feature Prioritization
Improves model focus on key sequence regions.
Robust Evaluation Metrics
Achieves high PCC and R² scores across 12 in-vitro datasets.

Techniques Used

Self-Attention Mechanism: Captures long-range dependencies for enhanced prediction accuracy.

Convolutional Neural Networks (CNNs): Identifies critical sequence motifs in DNA.

Bidirectional LSTM (BiLSTM): Captures sequential patterns and dependencies.

DNA Shape-Based Feature Augmentation: Incorporates DNA’s structural information.

Advanced Data Processing: Includes one-hot encoding, batch normalization, dropout, and L2 regularization.

📄 Reference Paper

Read More on Springer

Unlock the Power of AI in Gene Regulation

Enhance TFBS predictions with DeepCTF's cutting-edge deep learning approach.

Get in Touch

DeepCTF – AI-Powered TFBS Prediction

Enhancing gene regulation analysis with deep learning and DNA shape integration.

DeepCTF AI Model

🚀 Problem Statement: Traditional TFBS prediction models struggle with feature extraction, long-range dependencies, and integrating DNA shape information. DeepCTF addresses these limitations by leveraging deep learning techniques.

✨ Milestones Achieved

✅ High-Accuracy TFBS Prediction: DeepCTF outperforms traditional models with state-of-the-art performance.
✅ Hybrid Deep Learning Model: Combines CNN, BiLSTM, and self-attention for comprehensive sequence analysis.
✅ DNA Shape Feature Integration: Uses Minor Groove Width (MGW), Roll, Propeller Twist (ProT), and Helix Twist (HelT) for better accuracy.
✅ Attention Mechanism for Feature Prioritization: Improves model focus on key sequence regions.
✅ Robust Evaluation Metrics: Achieves high PCC and R² scores across 12 in-vitro datasets.

🔬 Techniques Used

🔹 Self-Attention Mechanism: Captures long-range dependencies for enhanced prediction accuracy.

🔹 Convolutional Neural Networks (CNNs): Identifies critical sequence motifs in DNA.

🔹 Bidirectional LSTM (BiLSTM): Captures sequential patterns and dependencies.

🔹 DNA Shape-Based Feature Augmentation: Incorporates DNA’s structural information.

🔹 Advanced Data Processing: Includes one-hot encoding, batch normalization, dropout, and L2 regularization.

📄 Reference Paper

Read More on Springer

Unlock the Power of AI in Gene Regulation

Enhance TFBS predictions with DeepCTF's cutting-edge deep learning approach.

Get in Touch