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The chordata olfactory receptor database

Protein & Cell 2025年第4期 283-292页

作者： Wei Han Siyu Bao Jintao Liu Yiran Wu Liting Zeng Tao Zhang Ningmeng Chen Kai Yao Shunguo Fan Aiping Huang Yuanyuan Feng Guiquan Zhang Ruiyi Zhang Hongjin Zhu Tian Hua Zhijie Liu Lina Cao Xingxu Huang Suwen Zhao iHuman Institute Shanghai Tech University Research Center for Life Sciences Computing Zhejiang Lab Department of Intelligent Edge Cloud China Telecom Cloud Technology Co. Ltd. School of Life Science and Technology Shanghai Tech University School of Information Science and Technology Shanghai Tech University Shanghai Key Laboratory of High-Resolution Electron Microscopy Shanghai Tech University Zhejiang Provincial Key Laboratory of Pancreatic Disease The First Affiliated Hospital and Institute of Translational Medicine Zhejiang University School of Medicine Shanghai Clinical Research and Trial Center

Introduction of database Olfaction is one of the oldest chemosensory systems in chordates, playing crucial roles in their foraging, predator evasion, social communication, mating and parental care (Guo et al., 2023; Li and Liberles, 2015; Liberles,2014). The initial step of olfaction is the binding and activation of olfactory receptors (ORs) by odorants in a combinatorial way (Malnic et al., 1999).

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CATI: A medical context-enhanced framework for diagnosis code assignment in the UK Biobank study

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Artificial Intelligence in Medicine 2025年 166卷 103136-103136页

作者： Shen, Yue Wang, Jie Wang, Zhe Shi, Zhihao Chen, Hanzhu Wang, Zheng Jiang, Yukang Wang, Xiaopu Cheng, Chuandong Wang, Xueqin Zhu, Hongtu Ye, Jieping MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Anhui Hefei230027 China Alibaba Cloud Computing Zhejiang Hangzhou310030 China Department of Radiology University of North Carolina at Chapel Hill NC27599 United States School of Management University of Science and Technology of China Anhui Hefei230027 China Biomedical Research Imaging Center Department of Biostatistics University of North Carolina at Chapel Hill NC27599 United States Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230027 China

Diagnosis codes are standard code format of diseases or medical conditions. This study is aimed at assigning diagnosis codes to patients in large-scale biobanks, particularly addressing the issue of missing codes for some patients. This is crucial for downstream disease-related tasks. While recent methods primarily rely on structured biobank data for code assignment, they often overlook the valuable medical context provided by textual information in the biobanks and hierarchical structure of the disease coding system. To address this gap, we have developed CATI, a medical context-enhanced framework for diagnosis Code Assignment by integrating Textual details derived from key features and disease hIerarchy. The study is based on the UK Biobank data and considers Phecodes and ICD-10 codes as standard disease formats. We start by representing ten informative codified features using their formal names and then integrate them into CATI as text embeddings, achieved through prompt tuning on the pre-trained language model BioBERT. Recognizing the hierarchical structure of diagnosis codes, we have developed a novel convolution layer in our method that effectively propagates logits between adjacent diagnosis codes. Evaluation results demonstrate that CATI outperforms existing state-of-the-art methods in terms of both Phecodes and ICD-10 codes, boasting at least a 5.16% improvement in average AUROC for unseen disease codes and an 8.68% rise in average AUPRC for disease codes with training instances ranging in (1000,10000]. This framework contributes to the formation of well-defined cohorts for downstream studies and offers a unique perspective for addressing complex healthcare tasks by incorporating vital medical context. © 2025

关键词： Diseases

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Multi-label feature selection via exploring reliable instance similarities

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Knowledge-Based Systems 2025年 322卷

作者： Cong Guo Xizhe Wang Changqin Huang Yi Wang Chengling Gao Xiaodi Huang Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China College of Education Zhejiang University Hangzhou China School of Computing Mathematics and Engineering Charles Sturt University Albury Australia

Existing multi-label feature selection methods commonly employ graph regularization to formulate sparse regression objectives based on manifold learning. However, these approaches face two primary challenges. First, they typically rely on fixed similarities derived from the original feature space, which can be unreliable due to irrelevant features. Second, while pseudo-labeling incorporates supervised information, traditional methods often neglect the global similarity structure among instances. To overcome these issues, we propose a two-stage iterative learning method that progressively refines instance similarities, mitigating the influence of irrelevant features. In the first stage (W-stage), we introduce reconstructed labels that integrate both global instance similarities and supervised information, enhancing the model’s capability to capture richer similarity structures among data points. We also integrate instance and label graph regularizations, coupled with an l 2 , 1 -norm constraint, into the objective function to optimize feature weights. In the second stage (S-stage), dynamically updated instance similarities derived from the reduced-dimensional space guide subsequent iterations, facilitating continuous refinement. These two stages collectively form a unified framework that iteratively enhances instance similarities within the reduced feature space while selecting relevant features. Experimental evaluations on 11 datasets demonstrate that our algorithm consistently outperforms eight state-of-the-art methods across six evaluation metrics. Moreover, our method achieves the highest overall performance ranking and exceeds the second-best approach by at least 2.19 ranks for each evaluation metric.

关键词： Semi-supervised learning

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Multimodal feature fusion based on object relation for video captioning

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CAAI Transactions on Intelligence technology 2023年第1期8卷 247-259页

作者： Zhiwen Yan Ying Chen Jinlong Song Jia Zhu School of Computing Science South China Normal UniversityGuangzhouChina Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhuaChina

Video captioning aims at automatically generating a natural language caption to describe the content of a ***,most of the existing methods in the video captioning task ignore the relationship between objects in the video and the correlation between multimodal features,and they also ignore the effect of caption length on the *** study proposes a novel video captioning framework(ORMF)based on the object relation graph and multimodal feature *** uses the similarity and Spatio-temporal relationship of objects in video to construct object relation features graph and introduce graph convolution network(GCN)to encode the object *** the same time,ORMF also constructs a multimodal features fusion network to learn the relationship between different modal *** multimodal feature fusion network is used to fuse the features of different ***,the proposed model calculates the length loss of the caption,making the caption get richer *** experimental results on two public datasets(Microsoft video captioning corpus[MSVD]and Microsoft research-video to text[MSR-VTT])demonstrate the effectiveness of our method.

关键词： approaches deep learning multimodel scene understanding video analysis

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Single Cell Clustering Based on Multi-view Generation 2nd

Single Cell Clustering Based on Multi-view Generation

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wang, Yueyue Wu, Zheyu Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819619061

The rapid development of single-cell RNA sequencing technology has opened new horizons for biomedical research. As the initial step in single-cell data analysis, clustering forms the foundation for downstream analyses. However, due to the inherent high dimensionality, noise, and sparsity of single-cell data, most current clustering methods exhibit poor stability. To address these limitations, we propose a novel clustering method based on multi-view generation (scCMVG) coupled with noise perturbation. Specifically, scCMVG introduces disturbances at both gene level and cellular level to disrupt the original associations between genes and cells. It then employs autoencoder and graph convolutional network to reconstruct the disturbed data and generate new feature representations from it. Through this strategy, the model's noise resistance can be enhanced, increasing the ability to capture diverse cellular states and gene expression patterns. Experimental results demonstrate scCMVG superior clustering performance across multiple single-cell RNA sequencing datasets, outperforming existing methods. Consequently, scCMVG emerges as a valuable tool for clustering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gene expression

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering 2nd

scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell ...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wu, Zheyu Wang, Yueyue Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9789819619061

Single-cell RNA sequencing provides high-throughput gene expression information and offers higher resolution of cell differences compared to bulk RNA sequencing, which helps identify cell types and explore cellular heterogeneity at the single-cell level. Although many single-cell clustering methods based on masked autoencoders have been developed recently, they rarely effectively utilize the correlations between cells, resulting in suboptimal clustering results. In this work, we propose a novel clustering method for scRNA-seq data based on a masked graph convolutional autoencoder, which incorporates a graph convolutional network onto the basis of a masked autoencoder to enhance the capture of the correlation between cells and genes by fusing the cell-cell interaction graph and the gene expression matrix. By increasing the relationships between cells, dimensionality reduction is guided, and a clustering-friendly representation is obtained. We conducted extensive comparative experiments using various clustering evaluation metrics on six scRNA-seq datasets from different sequencing platforms. The experimental results demonstrate that scMGCAE outperforms other methods on these datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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TLSTMF-YOLO: Transfer Learning and Feature Fusion Network for Earthquake-Induced Landslide Detection in Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Meng, Shaoqiang Shi, Zhenming Pirasteh, Saied Liberata Ullo, Silvia Peng, Ming Zhou, Changshi Nunes Goncalves, Wesley Zhang, Limin Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Key Laboratory of Geotechnical and Underground Engineering Ministry of Education Shanghai200092 China Institute of Artificial Intelligence Shaoxing University Zhejiang Shaoxing312000 China Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Geotechnics and Geomatics Tamil Nadu Chennai600 077 India University of Sannio Department of Engineering Benevento82100 Italy Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200092 China Federal University of Mato Grosso do Sul Faculty of Computing Campo Grande79070-900 Brazil The Hong Kong University of Science and Technology Department of Civil and Environmental Engineering Hong Kong Hong Kong

Detecting earthquake-induced landslides in remote sensing images is challenging due to the varying sizes of landslides, uneven distribution, and the prevalence of small targets. This study proposes a novel approach, the TLSTMF-YOLO model, which combines a C3-Swin-Transformer and multiscale feature fusion techniques to enhance detection accuracy and efficiency. key innovations include the use of a convolutional block attention module (CBAM) to improve feature representation, and a bidirectional feature pyramid network (BiFPN) for optimized cross-scale feature fusion. To address data scarcity, a transfer learning strategy is applied, supported by an AdamW optimizer and cosine learning rate strategy for faster convergence. Evaluations on the Jiuzhaigou and Luding landslide datasets demonstrate the model's effectiveness, achieving precision, recall, and mean average precision (mAP)@0.5 of 95.7%, 89.9%, and 90.5% on the Jiuzhaigou dataset, and 96.0%, 90.9%, and 94.5% on the Luding dataset, respectively. In addition, the model processes frames efficiently, with times of 6.61 and 12.2 ms on the two datasets. These results confirm the model's capability for accurate and efficient landslide detection, highlighting its potential for real-world applications. © 1980-2012 IEEE.

关键词： Earthquakes

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Research on context-aware computing with uncertainty for proactive service

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International Journal of Modelling, Identification and Control 2009年第3期8卷 248-255页

作者： Zhang, De-Gan Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin 300191 China Key Lab of Industrial Controlling Technology Zhejiang University Hangzhou 310027 China

It is known to all that context-aware computing with uncertainty is an important part of pervasive computing for proactive service. Because multi-source evidence context-aware information with uncertainty is dynamic and changing randomly, in order to ensure the QoS of different application fields based on pervasive computing, we modified the fusion method of evidence information after considering context's reliability, time efficiency and relativity, which has improved the classic fusion rule of D-S evidence theory when used in the pervasive computing paradigm. After extending the process, we overcome the shortcoming of classic D-S evidence theory. All these suggested technologies have been successfully used in our project. The efficiency of our researches has been tested by application practice and validation of the demo. Copyright © 2009, Inderscience Publishers.

关键词： Ubiquitous computing

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