IF 3.2 4区计算机科学

Journal of Mechanisms and Robotics-Transactions of the Asme Pub Date : 2025-11-01 Epub Date: 2025-08-04 DOI: 10.1115/1.4069154

Huan Liu, Qiaode Jeffrey Ge, Mark P Langer

{"title":"KINEMATIC CONVEX COMBINATIONS OF MULTIPLE POSES OF A BOUNDED PLANAR OBJECT BASED ON AN AVERAGE-DISTANCE MINIMIZING MOTION SWEEP.","authors":"Huan Liu, Qiaode Jeffrey Ge, Mark P Langer","doi":"10.1115/1.4069154","DOIUrl":"10.1115/1.4069154","url":null,"abstract":"<p><p>Convex combination of points is a fundamental operation in computational geometry. By considering rigid-body displacements as points in the image spaces of planar quaternions, quaternions and dual quaternions, respectively, the notion of convexity in Euclidean three-space has been extended to kinematic convexity in <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>2</mn> <mo>)</mo> <mo>,</mo> <mi>S</mi> <mi>O</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> , and <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> in the context of computational kinematic geometry. This paper deals with computational kinematic geometry of bounded planar objects rather than that of infinitely large moving spaces. In this paper, we present a new formulation for kinematic convexity based on an average-distance minimizing motion sweep of a bounded planar object. The resulting 1-DOF motion sweep between two planar poses is represented as a convex combination in the configuration space defined by <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo></math> where <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo></math> is associated with the location of the centroid of the planar object and <math><mi>z</mi> <mo>=</mo> <mtext>sin</mtext> <mspace></mspace> <mi>θ</mi></math> with <math><mi>θ</mi></math> being the angle of rotation. For three poses, a 2-DOF motion sweep is developed that not only minimizes the combined average squared distances but also attains a convex-combination representation so that existing algorithms for convex hull of points can be readily applied to the construction and analysis of kinematic convex hulls. This results in a new type of convex hull for planar kinematics such that its boundaries are defined by the average-distance minimizing sweeps of the bounded planar object.</p>","PeriodicalId":49155,"journal":{"name":"Journal of Mechanisms and Robotics-Transactions of the Asme","volume":"17 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12349904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PolarFusion: A multi-modal fusion algorithm for 3D object detection based on polar coordinates. PolarFusion：一种基于极坐标的三维目标检测多模态融合算法。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-10-01 Epub Date: 2025-06-18 DOI: 10.1016/j.neunet.2025.107704

Peicheng Shi, Runshuai Ge, Xinlong Dong, Chadia Chakir, Taonian Liang, Aixi Yang

{"title":"PolarFusion: A multi-modal fusion algorithm for 3D object detection based on polar coordinates.","authors":"Peicheng Shi, Runshuai Ge, Xinlong Dong, Chadia Chakir, Taonian Liang, Aixi Yang","doi":"10.1016/j.neunet.2025.107704","DOIUrl":"10.1016/j.neunet.2025.107704","url":null,"abstract":"<p><p>Existing 3D object detection algorithms that fuse multi-modal sensor information typically operate in Cartesian coordinates, which can lead to asymmetrical feature information and uneven attention across multiple views. To address this, we propose PolarFusion, the first multi-modal fusion BEV object detection algorithm based on polar coordinates. We designed three specialized modules for this approach: the Polar Region Candidates Generation Module, the Polar Region Query Generation Module, and the Polar Region Information Fusion Module. In the Polar Region Candidates Generation Module, we use a region proposal-based segmentation method to remove irrelevant areas from images, enhancing PolarFusion's information processing efficiency. These segmented image regions are then integrated into the point cloud segmentation task, addressing feature misalignment during fusion. The Polar Region Query Generation Module leverages prior information to generate high-quality target queries, reducing the time spent learning from initialization. For the Polar Region Information Fusion Module, PolarFusion employs a simple yet efficient self-attention to merge internal information from images and point clouds. This captures long-range dependencies in image texture information while preserving the precise positional data from point clouds, enabling more accurate BEV object detection. We conducted extensive experiments on challenging BEV object detection datasets. Both qualitative and quantitative results demonstrate that PolarFusion achieves an NDS of 76.1% and mAP of 74.5% on the nuScenes test set, significantly outperforming Cartesian-based methods. This advancement enhances the environmental perception capabilities of autonomous vehicles and contributes to the development of future intelligent transportation systems. The code will be released at https://github.com/RunshuaiGe/PolarFusion.git.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"107704"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DMSE: An efficient malicious traffic detection model based on deep multi-stacking ensemble learning DMSE：一种基于深度多堆叠集成学习的高效恶意流量检测模型

IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-09-06 DOI: 10.1007/s10489-025-06819-1

Saihua Cai, Yang Zhang, Yanghang Li, Yupeng Wang, Jiayao Li, Xiang Zhou

{"title":"DMSE: An efficient malicious traffic detection model based on deep multi-stacking ensemble learning","authors":"Saihua Cai, Yang Zhang, Yanghang Li, Yupeng Wang, Jiayao Li, Xiang Zhou","doi":"10.1007/s10489-025-06819-1","DOIUrl":"10.1007/s10489-025-06819-1","url":null,"abstract":"<div><p>In the context of increasing cyber threats, developing an efficient malicious traffic detection model to recognize the cyber attacks has become an urgent demand in the field of cyber security. This paper proposes an efficient malicious traffic detection model called DMSE based on deep multi-stacking ensemble learning, it is primarily consisted of feature representation module, base model detection module and multi-stacking ensemble learning module. In the feature representation phase, we propose a novel RGB image representation method, which hierarchically represents the global and local features of network traffic by allocating the information to three channels of RGB images. In the base model detection phase, we adopt five different deep learning models—CNN, TCN, LSTM, BiLSTM and BiTCN—as base models for the first-stage prediction. In the multi-stacking ensemble learning phase, we adopt the best-performing BiTCN from extensive experiments as the meta-learner to perform a second prediction using the results from the first stage, thereby obtaining the final detection result. Experiments conducted on USTC-TFC2016, CTU and ISAC datasets demonstrate that the proposed DMSE model significantly outperforms existing ensemble learning-based detection models in terms of accuracy, F1-score, false positive rate (FPR), true positive rate (TPR) and stability. The experimental results indicate that the proposed DMSE model can effectively identify and defend against network attacks, providing the new perspectives and technical support for maintaining a secure network environment.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Interpretability of NesT Model Using NesT-Shapley and Feature-Weight-Augmentation Method 利用NesT- shapley和特征权重增强方法增强NesT模型的可解释性

IF 1.3 4区计算机科学

IET Computer Vision Pub Date : 2025-09-06 DOI: 10.1049/cvi2.70039

Li Xu, Lei Li, Xiaohong Cong, Huijie Song

{"title":"Enhancing Interpretability of NesT Model Using NesT-Shapley and Feature-Weight-Augmentation Method","authors":"Li Xu, Lei Li, Xiaohong Cong, Huijie Song","doi":"10.1049/cvi2.70039","DOIUrl":"https://doi.org/10.1049/cvi2.70039","url":null,"abstract":"<p>The transformer's capabilities in natural language processing and computer vision are impressive, but interpretability is crucial in specific domain applications. The NesT model, with its pyramidal structure, demonstrates high accuracy and faster training speeds. Unlike other models, a unique aspect of NesT is its avoidance of the [CLS] token, which presents challenges when applying interpretability methods that rely on the model's internal structure. Instead, NesT divides the image into 16 blocks and processes them using 16 independent vision transformers. We propose the NesT-Shapley method, which utilises this structure to combine the Shapley value method (a self-interpretable approach) with the independently operating vision transformers within NesT, significantly reducing computational complexity. On the other hand, we introduced the feature weight augmentation (FWA) method to address the challenges of weight adjustment in the final interpretability results produced by interpretability methods without [CLS] token, markedly enhancing the performance of interpretability methods and providing a better understanding of the information flow during the prediction process in the NesT model. We conducted perturbation experiments on the NesT model using the ImageNet and CIFAR-100 datasets and segmentation experiments on the ImageNet-Segmentation dataset, achieving impressive experimental results.</p>","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"19 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An unsupervised domain adaptation method for cross-domain deceptive reviews detection 一种跨域欺骗性评论检测的无监督域自适应方法

IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-09-06 DOI: 10.1007/s10489-025-06825-3

Ning Cao, Shujuan Ji, Fuzhen Zhuang, Dickson K. W. Chiu, Yajie Guo, Maoguo Gong

{"title":"An unsupervised domain adaptation method for cross-domain deceptive reviews detection","authors":"Ning Cao, Shujuan Ji, Fuzhen Zhuang, Dickson K. W. Chiu, Yajie Guo, Maoguo Gong","doi":"10.1007/s10489-025-06825-3","DOIUrl":"10.1007/s10489-025-06825-3","url":null,"abstract":"<div><p>Deceptive reviews seriously affect the interests of consumers, honest sellers, and e-commerce platforms. As e-commerce platforms often involve multiple domains (i.e., different products or services), in-domain deceptive review detection models trained and tested on a specific dataset may not perform well on other domains. Moreover, obtaining annotated data for so many individual domains is unrealistic. Cross-domain deceptive review detection aims to leverage labeled source domain data to improve the model’s performance on unlabeled target domain data. However, existing cross-domain deceptive review detection methods require labels for target domain data or do not consider domain-specific information. To further advance research, this paper proposes an unsupervised domain adaptation method for detecting cross-domain deceptive reviews. First, we propose a multiple mask views generation method to enhance domain-specific information to obtain different mask views of reviews. Secondly, the BERT and mask attention mechanisms are used sequentially to obtain contextual representations of the mask views and the original view of reviews. Thirdly, to maintain the consistency between the mask views and the original view of reviews, we use the intra-domain Kullback-Leibler divergence to constrain their learning process. Moreover, we use inter-domain dynamic maximum mean discrepancy and conditional maximum mean discrepancy to reduce differences between the distribution of source and target domains. Three sets of experiments on two datasets show that our method is superior to the baselines. In particular, the impact of domain differences on domain adaptability is further analyzed according to the quantified metric named domain distance defined in this paper.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-view contrastive learning with Static attributes and Dynamic interests for Sequential Recommendation 基于静态属性和动态兴趣的序列推荐多视图对比学习

IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-09-06 DOI: 10.1007/s10489-025-06816-4

Mukun Chen, Jia Wu, Shirui Pan, Xiantao Cai, Bo Du, Wenbin Hu, Huiting Xu

{"title":"Multi-view contrastive learning with Static attributes and Dynamic interests for Sequential Recommendation","authors":"Mukun Chen, Jia Wu, Shirui Pan, Xiantao Cai, Bo Du, Wenbin Hu, Huiting Xu","doi":"10.1007/s10489-025-06816-4","DOIUrl":"10.1007/s10489-025-06816-4","url":null,"abstract":"<div><p>Sequential recommendation plays a critical role in preference prediction by capturing the temporal evolution of user behavior. However, a key challenge lies in effectively integrating static attributes, such as stable user traits and item properties, with dynamic interests, which reflect the users’ transient and evolving preferences during interactions with various items. Current approaches typically focus on static attributes or recent interactions, neglecting the nuanced interplay between long-term stability and short-term variability. Additionally, the disparate encoding strategies for various data structures—such as bipartite interaction graphs, heterogeneous knowledge graphs, and sequential data streams—lead to fragmented user and item representations, hindering the development of a unified framework and reducing the system’s ability to holistically model user preferences. To address these challenges, we propose the multi-view contrastive learning with <b>S</b>tatic attributes and Dynamic interests for Sequential Recommendation (SDSR), a novel framework that integrates static and dynamic characteristics to enhance recommendation systems. SDSR employs graph-based encoders to capture static user and item features, while a sequence encoder models temporal changes in user behavior. By leveraging contrastive learning, SDSR aligns representations across multiple data views—such as interaction graphs, knowledge graphs, and sequential data—creating a unified user-item model that bridges long-term preferences with short-term trends. It also ensures consistency across various representations, yielding a cohesive and robust framework for synthesizing multi-perspective data. Empirical evaluations on benchmark datasets demonstrate that SDSR significantly outperforms state-of-the-art models, validating its effectiveness in integrating multi-view data and capturing both static and dynamic user preferences.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Group Key Management System for Secure IoT Networks Using IT2FONC-HKM Approach 采用it2ffc - hkm方法的安全物联网混合组密钥管理系统

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-09-06 DOI: 10.1002/dac.70231

R. Regan, Rajasekar Rangasamy, B. V. Krishna, J. Manikandan

{"title":"A Hybrid Group Key Management System for Secure IoT Networks Using IT2FONC-HKM Approach","authors":"R. Regan, Rajasekar Rangasamy, B. V. Krishna, J. Manikandan","doi":"10.1002/dac.70231","DOIUrl":"https://doi.org/10.1002/dac.70231","url":null,"abstract":"<div>\u0000 \u0000 <p>Key Management System aids in safeguarding cryptographic keys within an organization, which becomes complex particularly in decentralized Internet of Things networks because of the wide range of devices and constant development of the structure of the network. Numerous existing research were made for efficient key management, but these methods face some, issues, such as the misbalancing of performance metrics and low security for important parameters in the data sharing. To overcome these problems, this work developed a Hybrid Group Key Management system that utilizes Interval Type 2 Fuzzy Logic and Optimal Non-Monopoly Search Strategy Boosted Coati (ONS-CO) algorithm named IT2FONC-HKM approach. The proposed work effectively manages qualitative and quantitative decision-making by combining Type 2 Fuzzy Logic. The ONS-CO affirms that unauthorized devices are rapidly eliminated from the network, which improves the performance of the key revocation process. The Hybrid Group Key Management system is more effective for resource-constrained environments. This work presents a comprehensive comparative analysis to appraise the effectiveness of the IT2FONC-HKM approach. The evaluation results demonstrate that the IT2FONC-HKM procures superior effectiveness with a high-security level of 99.2%, and low encryption and decryption times of 0.4 and 0.3 s. The Hybrid Group Key Management system offers a robust solution to safeguard communication in IoT networks and tackles the revocation issues faced by existing approaches.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 15","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized Federated Learning and Blockchain-Based Crowd Sensing for Secure 5G Vehicular Networks 优化联邦学习和基于区块链的安全5G车辆网络人群感知

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-09-06 DOI: 10.1002/dac.70212

Deepika Rani Sona, M. Leeban Moses, M. Ramkumar Raja, T. Perarasi

{"title":"Optimized Federated Learning and Blockchain-Based Crowd Sensing for Secure 5G Vehicular Networks","authors":"Deepika Rani Sona, M. Leeban Moses, M. Ramkumar Raja, T. Perarasi","doi":"10.1002/dac.70212","DOIUrl":"https://doi.org/10.1002/dac.70212","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent transportation systems (ITS) and the Internet of Vehicles (IoV) face significant challenges in ensuring data security, privacy, and low-latency communication for vehicular crowd sensing. These challenges are exacerbated by rapid node mobility, bursty interactions, and vulnerabilities in existing systems, such as unauthorized data access, delayed message transmission, and inefficient blockchain consensus. To address these challenges, an Optimized Federated Learning and Blockchain-Based Crowd Sensing for Secure 5G Vehicular Networks (OFGNN-BTCS-5G-IoV) is proposed in this paper. Here, the system model is initialized, and the federated generative adversarial network (FGAN) is employed to select relevant active miners and transactions. The FGAN is optimized using the pelican optimization algorithm (POA) to determine optimal parameters to decrease uploading delay. Then the blockchain architecture is used to enhance data storage, transparent validation, and efficient miner selection by addressing privacy and scalability challenges using the Lightweight Proof of Game (LPoG) consensus mechanism. The proposed OFGNN-BTCS-5G-IoV method is implemented in MATLAB, and the OFGNN-BTCS-5G-IoV achieves 20.28%, 28.22%, and 29.27% higher active miner efficiency with 18.26%, 15.22%, and 12.27% lower latency when compared with existing methods. By using FGAN, bio-inspired optimization, and blockchain, the OFGNN-BTCS-5G-IoV offers secure and low-latency vehicular crowd sensing for next-generation ITS.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 15","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Micro-dynamics prediction of well water level based on GRU and attention mechanism 基于GRU和关注机制的井水水位微动力学预测

IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-09-06 DOI: 10.1007/s10489-025-06855-x

Xiaoyu Fang, Lili Zhang, Haoran Li, Yaowen Zhang, Yunsheng Yao

引用次数: 0

Adaptive deep shared latent representation enables novel multi-omics cancer subtype classification 自适应深度共享潜在表征实现新的多组学癌症亚型分类

IF 3.5 2区计算机科学

Applied Intelligence Pub Date : 2025-09-05 DOI: 10.1007/s10489-025-06848-w

Min Li, Zhifang Qi, Shaobo Deng, Lei Wang, Xiang Yu

{"title":"Adaptive deep shared latent representation enables novel multi-omics cancer subtype classification","authors":"Min Li, Zhifang Qi, Shaobo Deng, Lei Wang, Xiang Yu","doi":"10.1007/s10489-025-06848-w","DOIUrl":"10.1007/s10489-025-06848-w","url":null,"abstract":"<div><p>Variations in outcomes among cancer patients are significant even when they have the same type of tumor. Identifying and classifying molecular subtypes of cancer offers a valuable opportunity to enhance prognosis and tailor treatment plans for individuals. Recent efforts have been made to generate extensive multidimensional genomic data to achieve this potential. However, existing algorithms still face challenges in integrating and analyzing such intricate datasets. In this study, we present Adaptive Deep Shared Latent Representation (ADSLR), a novel approach for cancer subtyping that utilizes shared latent representation to reveal distinct molecular subtypes in cancer. It incorporates a cycle autoencoder with a nonnegative matrix factorization layer, capturing consistent signals of nonlinear features at various omics levels. This enables the generation of adaptable representations for shared latent representation across multiple omics levels. We apply ADSLR to multi-omics data obtained from eight different cancer types in the “The Cancer Genome Atlas” dataset, demonstrating significant improvements in the identification of biologically meaningful cancer subtypes. These identified subtypes exhibit noteworthy variations in patient survival rates across seven out of the eight cancer types. Our analysis uncovers integrated patterns involving mRNA expression, miRNA expression, DNA methylation, and protein across multiple cancers while showcasing ADSLR’s versatility for integrating various other omics types.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

计算机科学最新文献