Prediction of the tetramer protein complex interaction based on CNN and SVM

TCellR2Vec: efficient feature selection for TCR sequences for cancer classification

Cancer remains one of the leading causes of death globally. New immunotherapies that harness the patient's immune system to fight cancer show promise, but their development requires analyzing the diversity of immune cells called T-cells. T-cells have receptors that recognize and bind to cancer cells. Sequencing these T-cell receptors allows to provide insights into their immune response, but extracting useful information is challenging. In this study, we propose a new computational method, TCellR2Vec, to select key features from T-cell receptor sequences for classifying different cancer types. We extracted features like amino acid composition, charge, and diversity measures and combined them with other sequence embedding techniques. For our experiments, we used a dataset of over 50,000 T-cell receptor sequences from five cancer types, which showed that TCellR2Vec improved classification accuracy and efficiency over baseline methods. These results demonstrate TCellR2Vec's ability to capture informative aspects of complex T-cell receptor sequences. By improving computational analysis of the immune response, TCellR2Vec could aid the development of personalized immunotherapies tailored to each patient's T-cells. This has important implications for creating more effective cancer treatments based on the individual's immune system.

User emotion recognition and indoor space interaction design: a CNN model optimized by multimodal weighted networks

In interior interaction design, achieving intelligent user-interior interaction is contingent upon understanding the user's emotional responses. Precise identification of the user's visual emotions holds paramount importance. Current visual emotion recognition methods rely solely on singular features, predominantly facial expressions, resulting in inadequate coverage of visual characteristics and low recognition rates. This study introduces a deep learning-based multimodal weighting network model to address this challenge. The model initiates with a convolutional attention module, employing a self-attention mechanism within a convolutional neural network (CNN). As a result, the multimodal weighting network model is integrated to optimize weights during training. Finally, a weight network classifier is derived from these optimized weights to facilitate visual emotion recognition. Experimental outcomes reveal a 77.057% correctness rate and a 74.75% accuracy rate in visual emotion recognition. Comparative analysis against existing models demonstrates the superiority of the multimodal weight network model, showcasing its potential to enhance human-centric and intelligent indoor interaction design.

Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches

Protein-protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein-protein docking. With the rapid development of artificial intelligence and its great success in monomer protein structure prediction, deep learning has widely been applied to modeling protein-protein complex structures through inter-protein contact prediction and end-to-end approaches in the past few years. This article reviews the recent advances of deep-learning-based approaches in modeling protein-protein complex structures as well as their advantages and limitations. Challenges and possible future directions are also briefly discussed in applying deep learning for the prediction of protein complex structures.