Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption prediction module. The simulation environment is constructed using real-world vessel movement data and enhanced with a Diffusion Model to simulate dynamic maritime conditions. Vessel fuel consumption is estimated using historical operational data and learning-based regression. The surrounding environment is represented as image-based inputs to capture spatial complexity. We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion. This framework effectively handles complex tasks progressively while ensuring stable and efficient learning in continuous action spaces. We validate the proposed approach in a sea area of the Indian Ocean, demonstrating its efficacy in enabling sustainable and safe vessel navigation.

Coordinating traffic signals along multimodal corridors is challenging because many multi-agent deep reinforcement learning (DRL) approaches remain vehicle-centric and struggle with high-dimensional discrete action spaces. We propose a Multi-Agent Action-Branching Double Deep Q-Network (MA2B-DDQN), a human-centric framework that explicitly optimizes traveler-level equity. Our key contribution is an action-branching discrete control formulation that decomposes corridor control into (i) local, per-intersection actions that allocate green time between the next two phases and (ii) a single global action that selects the total duration of those phases. This decomposition enables scalable coordination under discrete control while reducing the effective complexity of joint decision-making. We also design a human-centric reward that penalizes the number of delayed individuals in the corridor, accounting for pedestrians, vehicle occupants, and transit passengers. Extensive evaluations across seven realistic traffic scenarios in Melbourne, Australia, demonstrate that our approach significantly reduces the number of impacted travelers, outperforming existing DRL and baseline methods. Experiments confirm the robustness of our model, showing minimal variance across diverse settings. This framework not only advocates for a fairer traffic signal system but also provides a scalable solution adaptable to varied urban traffic conditions.

Autonomous aerial vehicles (AAVs) are widely used for ship tracking and detection tasks. However, the real-time detection performance is limited by AAV battery capacity and computing power, resulting in a short operational duration. To address this challenge, this paper proposes a AAV ship detection system that focuses on two key aspects: algorithm improvement and computational resource allocation. Specifically, we introduce a lightweight ship detection method tailored for multi-AAV scenarios in a mobile edge computing environment. The proposed method first designs a multi-disentangled knowledge distillation approach based on an information decoupling framework and utilizes a newly designed teacher network to enhance the lightweight detection model. The teacher network disentangles two key types of entanglements: the relationship between the convolutional filters and target categories, and the relationship between the foreground and background regions in the feature maps. Additionally, a proximal policy optimization (PPO) reinforcement learning algorithm is designed to enable real-time decision-making for AAV motion, detection accuracy, and computational offloading. Finally, we validate the superiority of the proposed knowledge distillation method and demonstrate the robustness and effectiveness of the AAV path planning algorithm in various scenarios through a series of experiments. Compared to the improved student models YOLOv8-N and YOLOv10-N, our method improves [email protected] by 1.2% and 1.1% on the SeaShips7000 and FVessel validation sets. Furthermore, compared to the existing methods K-Means and DBSCAN, our approach achieves reward values approximately 2.0 times and 1.4 times higher, respectively.

Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.

Accurate and reliable traffic flow prediction is difficult due to the highly nonlinear, complex, and stochastic natures of urban traffic flow data, but its solutions are critically important for intelligent transportation systems (ITSs) and Internet of Things (IoT). In this study, a novel deep learning framework, named adaptive reinitialized differential evolution (ARDE)-neural basis expansion analysis for time-series forecasting (N-BEATS), is proposed to address this challenge. With the framework of ARDE-N-BEATS, first, an N-BEATS-based deep learning architecture is formulated for modeling traffic flow data. Second, a novel enhanced evolutionary algorithm, termed ARDE, is presented for optimizing the hyperparameter and structure of N-BEATS. Compared to the vanilla differential evolution (DE) algorithm, ARDE exhibits faster convergence and stronger searching capabilities. Experiments on three real-world traffic flow data sets from Dublin and San Francisco demonstrate that ARDE-N-BEATS can achieve high accuracy of at least 94% for most of the predictions, and outperforms the existing counterpart methods. A comparison between different hyperparameter optimization approaches further reveals that ARDE provides better or very competitive predictions and saves as high as 78.90% of computational expense.

The growing availability of maritime IoT traffic data and continuous expansion of the maritime traffic volume, serving as the driving fuel, propel the latest Artificial Intelligence (AI) studies in the maritime domain. Among the most recent advancements, vessel trajectory prediction is one of the most essential topics for assuring maritime transportation safety, intelligence, and efficiency. This paper presents an up-to-date review of existing approaches, including state-of-the-art deep learning, for vessel trajectory prediction. We provide a detailed explanation of data sources and methodologies used in the vessel trajectory prediction studies, highlight a discussion regarding the auxiliary techniques, complexity analysis, benchmarking, performance evaluation, and performance improvement for vessel trajectory prediction research, and finally summarize the current challenges and future research directions in this field.

SUMMARY: Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. AVAILABILITY AND IMPLEMENTATION: https://github.com/yuansliu/minirmd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The accurate and timely destination prediction of taxis is of great importance for location-based service applications. Over the last few decades, the popularization of vehicle navigation systems has brought the era of big data to the taxi industry. Existing destination prediction approaches are mainly based on various Markov chain models or trip matching ideas, which require geographical information and may encounter the problem of data sparsity. Other machine learning prediction models are still unsatisfactory in providing favorable results. In this paper, first, we propose use of a novel and efficient data embedding method for time-related feature pre-processing. The key idea behind this is to embed the data into a two-dimensional space before feature selection. Second, we propose use of a novel data-driven ensemble learning approach for destination prediction. This approach combines the respective superiorities of support vector regression and deep learning at different segments of the whole trajectory. Our experiments are conducted on two real data sets to demonstrate that the proposed ensemble learning model can get superior performance for taxi destination prediction. Comparisons also confirm the effectiveness of the proposed data embedding method in the deep learning model.

BACKGROUND: Drug-drug interactions (DDIs) are a major concern in patients' medication. It's unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples. RESULTS: To address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs. CONCLUSIONS: The results demonstrate that positive-unlabeled learning paves a new way to tackle the problem caused by the lack of experimentally verified negatives in the computational prediction of DDIs.

BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS: We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (≥0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS: Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement.

The in silico prediction of potential drug-targetinteractions is of critical importance in drug research. Existing computational methods have achieved remarkable prediction accuracy, however usually obtain poor prediction efficiency due to computational problems. To improve the prediction efficiency, we propose to predict drug targets based on inte- gration of heterogeneous features with anchor graph hashing and ensemble learning. First, we encode each drug as a 5682- bit vector, and each target as a 4198-bit vector using their heterogeneous features respectively. Then, these vectors are embedded into low-dimensional Hamming Space using anchor graph hashing. Next, we append hashing bits of a target to hashing bits of a drug as a vector to represent the drug-target pair. Finally, vectors of positive samples composed of known drug-target pairs and randomly selected negative samples are used to train and evaluate the ensemble learning model. The performance of the proposed method is evaluated on simulative target prediction of 1094 drugs from DrugBank. Extensive comparison experiments demonstrate that the proposed method can achieve high prediction efficiency while preserving satisfactory accuracy. In fact, it is 99.3 times faster and only 0.001 less in AUC than the best literature method “Pairwise Kernel Method”.

BACKGROUND: Early and accurate identification of potential adverse drug reactions (ADRs) for combined medication is vital for public health. Existing methods either rely on expensive wet-lab experiments or detecting existing associations from related records. Thus, they inevitably suffer under-reporting, delays in reporting, and inability to detect ADRs for new and rare drugs. The current application of machine learning methods is severely impeded by the lack of proper drug representation and credible negative samples. Therefore, a method to represent drugs properly and to select credible negative samples becomes vital in applying machine learning methods to this problem. RESULTS: In this work, we propose a machine learning method to predict ADRs of combined medication from pharmacologic databases by building up highly-credible negative samples (HCNS-ADR). Specifically, we fuse heterogeneous information from different databases and represent each drug as a multi-dimensional vector according to its chemical substructures, target proteins, substituents, and related pathways first. Then, a drug-pair vector is obtained by appending the vector of one drug to the other. Next, we construct a drug-disease-gene network and devise a scoring method to measure the interaction probability of every drug pair via network analysis. Drug pairs with lower interaction probability are preferentially selected as negative samples. Following that, the validated positive samples and the selected credible negative samples are projected into a lower-dimensional space using the principal component analysis. Finally, a classifier is built for each ADR using its positive and negative samples with reduced dimensions. The performance of the proposed method is evaluated on simulative prediction for 1276 ADRs and 1048 drugs, comparing using four machine learning algorithms and with two baseline approaches. Extensive experiments show that the proposed way to represent drugs characterizes drugs accurately. With highly-credible negative samples selected by HCNS-ADR, the four machine learning algorithms achieve significant performance improvements. HCNS-ADR is also shown to be able to predict both known and novel drug-drug-ADR associations, outperforming two other baseline approaches significantly. CONCLUSIONS: The results demonstrate that integration of different drug properties to represent drugs are valuable for ADR prediction of combined medication and the selection of highly-credible negative samples can significantly improve the prediction performance.