Cancer driver genes play a key role in tumor development, and the mutation or aberrant expression of these genes, which mainly occurs at the somatic cell level, has become a focus of research in recent years. Identifying lung cancer driver genes from millions of somatic cell mutations is undoubtedly a challenging task. In order to accurately predict lung cancer driver genes, researchers have proposed numerous computational methods, most of which employ machine learning techniques such as random forests and support vector machines (SVMs). However, simply merging features extracted from different data sources and using the selected features for training machine learning models is not an optimal strategy. New research points out that gene expression data can effectively characterize the similarity between genes. Based on this finding, an advanced deep learning-based analysis method, BiLSTM-Driver is proposed which significantly improves the prediction accuracy of lung cancer driver genes by probing deeply into the mutational features of genes and their neighbors through the Bidirectional Long Short-Term Memory networks. This method performs well in lung cancer research, especially in the prediction of lung adenocarcinoma and lung squamous carcinoma, and achieves better performance in AUC performance, precision and recall, achieving the performance of 0.994,0.991, and 0.987, and 0.987,0.993, and 0.991,respectively, which is significantly better than other algorithms. The proposed method BiLSTM-Driver provides an innovative way to identify new lung cancer driver genes. Through in-depth analysis of lung cancer-related genomic data, BiLSTM-Driver is able to accurately identify potential oncogenes and reveal their key roles in lung cancer development.

Non-Small Cell Lung Cancer(NSCLC) is characterized by poor prognosis and high mortality rates, making the prediction of survival duration crucial for NSCLC patients. Traditional cancer survival prediction relies mainly on the analysis of clinical and pathological characteristics, but these pieces of information often fail to fully reveal the complexity of the disease, thereby limiting the accuracy and reliability of survival prediction. Current deep learning techniques face several challenges, including inadequate utilization of data modalities, only predicting patient survival outcomes, and predicting shorter survival periods for patients. To address these issues, a multimodal fusion neural network, MTPSN (Multimodal Transformer and Convolutional Neural Network for Survival Prediction in Non-Small Cell Lung Cancer Patients), was designed. This model combines convolutional neural networks (CNNs) and transformers to predict the survival duration of NSCLC patients. The model uses CNNs to process multi-modal and high-dimensional genetic data, including clinical information, mRNA, miRNA, copy number variations, and DNA methylation, effectively extracting feature representations. By integrating and fusing these multi-modal data features, the model can utilize the fused feature representations to predict the survival duration of NSCLC patients. Experimental results demonstrate that on squamous cell carcinoma and adenocarcinoma datasets, the proposed survival prediction method achieves a Concordance Index (C-index) of 0.748 and an Integrated Brier Score (IBS) of 0.172, outperforming existing survival prediction models significantly.

The uncertainty in maritime transportation dramatically impacts liner shipping efficiency; to improve operational stability, timely recovery of vessel schedule in liner shipping service is essential after disruptions; however, recovery actions usually cause vessel carbon emissions to increase. In order to reduce the effect of disruption and carbon emissions of liner shipping, in this study, we develop a mixed-integer nonlinear programming mathematical model that contains three recovery strategies and considers the costs of the vessel’s voyage in liner shipping service, with emphasis on taking into account the carbon emission and container cumulative delay costs. An adaptive genetic algorithm (RLSGA) is developed to solve the model based on reinforcement learning and simulated annealing algorithm. Numerical experiments are conducted on a real liner shipping route to investigate the effectiveness of the model and method. The results indicate that compared with the CPLEX, the proposed RLSGA can provide a more effective vessel schedule recovery solution after disruption occurrences within 20 seconds of all test cases and has a less than 1% deviation rate. Implementing the model in the liner shipping service can lead to a reduction in carbon emissions, cost savings on voyages, and an obvious decrease in the number of containers affected by disruptions. Moreover, this work also analyzes the potential of clean fuel on vessel schedule recovery and obtains managerial insights. The research findings can provide a meaningful reference for liner shipping companies in operation decision-making.

In recent years, DNA strand displacement technology has become an integral part of DNA computing, which is proved that the complement circuit played an important role in computer circuits. In this paper, a four-bit complement logic circuit based on DNA strand displacement is designed and simulated. The simulation results show that the designed circuit is reliable and the four-bit complement logic circuit based on DNA strand displacement also indicates that the DNA strand displacement has bright future in the construction of large-scale logic circuits.