检索结果-内蒙古大学图书馆

Identification of Key Genes as Potential Drug Targets for Gastric Cancer

Tsinghua science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

来源：评论

学校读者我要写书评

暂无评论

Multiphoton Resonance Meets Tunneling Ionization: high-Efficient Photoexcitation in Strong-Field-Dressed Ions

引用

Physical Review Letters 2024年第11期133卷 113201页

作者： Yewei Chen Hongbin Lei Quanjun Wang Hongqiang Xie He Zhang Xu Lu Ning Zhang Shunlin Huang Yuzhu Wu Jianpeng Liu Qian Zhang Yi Liu Zengxiu Zhao Jing Zhao Jinping Yao State Key Laboratory of High Field Laser Physics School of Optical-Electrical and Computer Engineering Department of Physics School of Science Center of Materials Science and Optoelectronics Engineering School of Microelectronics

Tunneling ionization, a fascinating quantum phenomenon, has played the key role in the development of attosecond physics. Upon absorption of a few tens of photons, tunneling ionization creates ions in different excited states and even enables the formation of population inversion between ionic states. However, the underlying physics is still being debated. Here, we demonstrate a significant enhancement in the efficiency of multiphoton excitation when ionization of neutral molecules and resonant excitation of ions coexist in strong laser fields. It facilitates the dramatic increase in population inversion and lasing radiation in N2+ around 1000 nm pump wavelength. Utilizing the ionization-coupling theory, we discover that the synergistic interplay between tunneling ionization and multiphoton excitation enables the ionic coherence to be maximized by phase locking of the periodically created ionic dipoles and consistently maintain an optimal phase for the follow-up photoexcitation. This Letter provides new insights into the photoexcitation mechanism of ions in strong laser fields and opens up a route for optimizing ionic lasing radiations.

关键词： Multiphoton or tunneling ionization & excitation Optical transient phenomena Stark effect Ultrafast phenomena

来源：评论

学校读者我要写书评

暂无评论

FedCA: Federated learning based on classification layer alignment

FedCA: Federated learning based on classification layer alig...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Gang Liu Jing Jia Rui Mao Yan Ji Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

The issue of data heterogeneity has long been a significant challenge in federated learning, impacting the speed of global model convergence and potentially lowering the overall performance of the global model, thereby impeding its development. In response to this challenge, we propose a classifier head-based method to correct the local model update direction. Unlike most existing calibration methods, our method not only ensures a global model with good performance but also guarantees the performance of a single local model. Concretely, we have introduced a new federated learning framework called FedCA. In this framework, the output of the feature extractor is imported into both a global classifier and a local classifier. The gradients are calculated separately, and then backpropagate into the model. This method ensures the performance of the local model and allows the client to maintain a consistent learning direction. Our experimental results demonstrate the feasibility of this method.

关键词： Training Federated learning Feature extraction Data models Classification algorithms Calibration Convergence

来源：评论

学校读者我要写书评

暂无评论

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

引用

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

来源：评论

学校读者我要写书评

暂无评论

Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

引用

Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Manufacturing Process Selection Using Graph Neural Networks: A Novel Approach to CAD Model Semantic Segmentation 4

Manufacturing Process Selection Using Graph Neural Networks:...

引用

4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Niu, Shuai Cai, Yishu Li, Feng Li, Yibo Tong, Xiaomeng School of Automation Science and Electrical Engineering Beijing China University of Liverpool Department of Computer Science Liverpool United Kingdom AVIC Shenyang Aircraft Corporation Shenyang China Central South University State Key Laboratory of Precision Manufacturing for Extreme Service Performance Changsha China

ISBN: (纸本)9798331541729

Manufacturing process selection, as a core element in the manufacturing workflow, plays a crucial role in improving processing efficiency and manufacturing quality. However, due to the fragmented, experience-based, and often outdated nature of processing knowledge, current process selection methods heavily rely on human intervention. To address these challenges, this study proposes an innovative approach that reframes manufacturing process selection as a graph-based semantic segmentation task. Building on this concept, we introduce a graph neural network-based method for manufacturing process selection, capable of effectively identifying processing methods in CAD models. This method discretely samples the topological faces and edges of CAD models, storing them as node and edge attributes in an attributed adjacency graph. Finally, we design experiments to validate the effectiveness and accuracy of this method in identifying manufacturing processes. Experimental results demonstrate that representing 3D CAD models with attributed adjacency graphs effectively captures the geometric and topological relationships between different faces of the model. This method can accurately identify corresponding manufacturing processes at the topological face level of CAD models, offering a novel approach to automated process selection. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

SpiralShard: highly Concurrent and Secure Blockchain Sharding via Linked Cross-shard Endorsement

arXiv

引用

arXiv 2024年

作者： Lin, You Li, Mingzhe Zhang, Jin The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Institute of High Performance Computing A*STAR Singapore

Blockchain sharding improves the scalability of blockchain systems by partitioning the whole blockchain state, nodes, and transaction workloads into different shards. However, existing blockchain sharding systems generally suffer from a small number of shards, resulting in limited concurrency. The main reason is that existing sharding systems require large shard sizes to ensure security. To enhance the concurrency of blockchain sharding securely, we propose SpiralShard. The intuition is to allow the existence of some shards with a larger fraction of malicious nodes (i.e., corrupted shards), thus reducing shard sizes. SpiralShard can configure more and smaller shards for higher concurrency at the same network size. To ensure security with the existence of corrupted shards, we propose the Linked Cross-shard Endorsement (LCE) protocol. According to our LCE protocol, the blocks of each shard are sequentially verified and endorsed by a group of shards before being finalized. As a result, a corrupted shard can eliminate forks with the help of the other shards. We implement SpiralShard based on Harmony and conduct extensive evaluations. Experimental results show that, compared with Harmony, SpiralShard achieves around 19× throughput gain under a large network size with 4,000+ nodes. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Cultural-Aware AI Model for Emotion Recognition

Cultural-Aware AI Model for Emotion Recognition

引用

Iranian Machine Vision and Image Processing (MVIP)

作者： Mehrdad Baradaran Payam Zohari Abtin Mahyar Hossein Motamednia Dara Rahmati Saeid Gorgin Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran Department of Computer Engineering Khaje Nasir University Of Technology Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Chosun University Gwangju South Korea

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multimodal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Positive Impact of Artificial Intelligence on Education 1

The Positive Impact of Artificial Intelligence on Education

引用

1st International Conference on Global Aeronautical engineering and Satellite Technology, GAST 2024

作者： Gourari, Abdelali El Raoufi, Mustapha Skouri, Mohammed Ben Braim, Abdelghani Ait Ezzini, Mustapha Lahsen, Yassine Ait Achki, Samira Cadi Ayyad University Laboratory of Physics High Energy and Astrophysics Faculty of Science Semlalia Marrakech Morocco Cadi Ayyad University Laboratory of Materials Energy and Environment Faculty of Sciences Semlalia Marrakech Morocco Cadi Ayyad University Faculty of Sciences Semlalia Fluid Mechanics and Energetic Laboratory Marrakesh Morocco Cadi Ayyad University Laboratory of Computer Systems Engineering Faculty of Sciences Semlalia Marrakech Morocco Fstg Computer Science Department Marrakech Morocco

ISBN: (纸本)9798350371598

Those interested in artificial intelligence technologies, especially supervised and unsupervised learning in education, know they need considerable data for well-modeled training and high-quality accuracy. However, data access is not easy as it drains time and effort. So, in this paper, we will talk about the impact of artificial intelligence on education in general and how it will affect teachers' role in the future. We will also create an algorithm that will integrate several algorithms sequentially that will add value to this paper and contribute significantly to solving most of the problems that exist in education. Since the reinforcement learning (RL) algorithm is based on trial and error during training, it stores all the good events done in a considerable buffer. Once the RL model is ready, we will collect enough data to be used to train one of the algorithms mentioned earlier (supervised learning and unsupervised learning). © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Disentangled latent factors for muti-cause treatment effect estimation 6

Disentangled latent factors for muti-cause treatment effect ...

引用

2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhao, Zhuoyue Wang, Haotian Xu, Liyang Wang, Qian Tang, Yuhua Department of Intelligent Data Science National University of Defense Technology Changsha410073 China Intelligent Game and Decision Lab. Academy of Military Science Beijing100071 China Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation. © 2024 Institute of Physics Publishing. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：