Browsed by
Category: machine-learning

machine-learning

Enhancing CRISPR/Cas9 Precision: A Comparative Analysis of Deep Learning Models for Off-Target Prediction

Enhancing CRISPR/Cas9 Precision: A Comparative Analysis of Deep Learning Models for Off-Target Prediction

Enhancing CRISPR/Cas9 Precision: A Comparative Analysis of Deep Learning Models for Off-Target Prediction

Enhancing CRISPR/Cas9 Precision: A Comparative Analysis of Deep Learning Models for Off-Target Prediction
Enhancing CRISPR/Cas9 Precision: A Comparative Analysis of Deep Learning Models for Off-Target Prediction

CRISPR/Cas9 gene editing technology holds immense therapeutic potential, offering precise control over genetic modifications. However, off-target effects—unintended edits at genomic locations similar to the target site—represent a significant hurdle, particularly in clinical settings. Mitigating these risks requires robust prediction methods, and deep learning has emerged as a powerful tool in this endeavor. This analysis reviews the application of deep learning models to predict CRISPR/Cas9 off-target sites (OTS), comparing their performance and identifying key factors influencing their accuracy.

Several deep learning models have been developed to predict potential OTS based on sequence features. This study focuses on six prominent models: CRISPR-Net, CRISPR-IP, R-CRISPR, CRISPR-M, CrisprDNT, and Crispr-SGRU. We evaluated these models using six publicly available datasets, supplemented by validated OTS data from the CRISPRoffT database. Performance was rigorously assessed using a suite of standardized metrics, including Precision, Recall, F1-score, Matthews Correlation Coefficient (MCC), Area Under the Receiver Operating Characteristic curve (AUROC), and Area Under the Precision-Recall curve (PRAUC).

Our comparative analysis revealed a significant impact of training data quality on model performance. The incorporation of validated OTS datasets demonstrably enhanced both the overall accuracy and robustness of predictions, particularly when addressing the inherent class imbalance often found in OTS datasets (where true off-targets are significantly less frequent than true on-targets). While no single model consistently outperformed others across all datasets, CRISPR-Net, R-CRISPR, and Crispr-SGRU consistently demonstrated strong overall performance, highlighting the potential of specific architectural designs.

This comprehensive evaluation underscores the critical need for high-quality, validated OTS data in training deep learning models for CRISPR/Cas9 off-target prediction. The integration of such data with sophisticated deep learning architectures is crucial for improving the accuracy and reliability of these predictive tools, ultimately contributing to the safe and effective application of CRISPR/Cas9 technology in therapeutic and research contexts. Future research should focus on developing even more robust models and expanding the availability of high-quality, experimentally validated OTS datasets to further enhance predictive capabilities.

阅读中文版 (Read Chinese Version)

Disclaimer: This content is aggregated from public sources online. Please verify information independently. If you believe your rights have been infringed, contact us for removal.

Optimizing Multi-Stream Convolutional Neural Networks: Enhanced Feature Extraction and Computational Efficiency

Optimizing Multi-Stream Convolutional Neural Networks: Enhanced Feature Extraction and Computational Efficiency

Optimizing Multi-Stream Convolutional Neural Networks: Enhanced Feature Extraction and Computational Efficiency

A vibrant and artistic representation of neural networks in an abstract 3D render, showcasing technology concepts.
A vibrant and artistic representation of neural networks in an abstract 3D render, showcasing technology concepts.

The rapid advancement of artificial intelligence (AI) has propelled deep learning (DL) to the forefront of technological innovation, particularly in computer vision, natural language processing, and speech recognition. Convolutional neural networks (CNNs), a cornerstone of DL, have demonstrated exceptional performance in image processing and pattern recognition. However, traditional single-stream CNN architectures face limitations in computational efficiency and processing capacity when dealing with increasingly complex tasks and large-scale datasets.

Multi-stream convolutional neural networks (MSCNNs) offer a promising alternative, leveraging parallel processing across multiple paths to enhance feature extraction and model robustness. This study addresses significant shortcomings in existing MSCNN architectures, including isolated information between paths, inefficient feature fusion mechanisms, and high computational complexity. These deficiencies often lead to suboptimal performance in key robustness indicators such as noise resistance, occlusion sensitivity, and resistance to adversarial attacks. Furthermore, current MSCNNs often struggle with data and resource scalability.

To overcome these limitations, this research proposes an optimized MSCNN architecture incorporating several key innovations. A dynamic path cooperation mechanism, employing a novel path attention mechanism and a feature-sharing module, fosters enhanced information interaction between parallel paths. This is coupled with a self-attention-based feature fusion method to improve the efficiency of feature integration. Furthermore, the optimized model integrates path selection and model pruning techniques to achieve a balanced trade-off between model performance and computational resource demands.

The efficacy of the proposed optimized model was rigorously evaluated using three datasets: CIFAR-10, ImageNet, and a custom dataset. Comparative analysis against established models such as Swin Transformer, ConvNeXt, and EfficientNetV2 demonstrated significant improvements across multiple metrics. Specifically, the optimized model achieved superior classification accuracy, precision, recall, and F1-score. Furthermore, it exhibited substantially faster training and inference times, reduced parameter counts, and lower GPU memory usage, highlighting its enhanced computational efficiency.

Simulation experiments further validated the model’s robustness and scalability. The optimized model demonstrated significantly improved noise robustness, occlusion sensitivity, and resistance to adversarial attacks. Its data scalability efficiency and task adaptability were also superior to the baseline models. This improved performance is attributed to the integrated path cooperation mechanism, the self-attention-based feature fusion, and the implemented lightweight optimization strategies. These enhancements enable the model to effectively handle complex inputs, adapt to diverse tasks, and operate efficiently in resource-constrained environments.

While this study presents significant advancements in MSCNN optimization, limitations remain. The fixed three-path architecture may limit adaptability to highly complex tasks. The computational overhead of the self-attention mechanism presents a challenge for real-time applications. Future research will focus on developing dynamic path adjustment mechanisms, exploring more computationally efficient feature fusion techniques, and expanding the model’s applicability to more complex tasks, such as semantic segmentation and small-sample learning scenarios.

In conclusion, this research provides a valuable contribution to the field of deep learning architecture optimization. The proposed optimized MSCNN architecture demonstrates superior performance, robustness, and scalability, offering a significant advancement for various applications requiring efficient and robust deep learning models. The findings contribute to a more comprehensive understanding of MSCNNs and pave the way for future research in dynamic path allocation, lightweight feature fusion, and broader task applicability.

阅读中文版 (Read Chinese Version)

Disclaimer: This content is aggregated from public sources online. Please verify information independently. If you believe your rights have been infringed, contact us for removal.