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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

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Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate Adaptation

arXiv

引用

arXiv 2024年

作者： Su, Hairong Wang, Shibo Yang, Shusen Huang, Tianchi Ren, Xuebin National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University China R&D department Sony

Bitrate adaptation (also known as ABR) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABR algorithms suffer from severe traffic wastage, which refers to the traffic cost of downloading the video segments that users do not finally consume, for example, due to early departure or video skipping. In this paper, we carefully formulate the dynamics of buffered data volume (BDV), a strongly correlated indicator of traffic wastage, which, to the best of our knowledge, is the first time to rigorously clarify the effect of downloading plans on potential wastage. To reduce wastage while keeping a high QoE, we present a bandwidth-efficient bitrate adaptation algorithm (named BEABR), achieving consistently low BDV without distinct QoE losses. Specifically, we design a precise, time-aware transmission delay prediction model over the Transformer architecture, and develop a fine-grained buffer control scheme. Through extensive experiments conducted on emulated and real network environments including WiFi, 4G, and 5G, we demonstrate that BE-ABR performs well in both QoE and bandwidth savings, enabling a 60.87% wastage reduction and a comparable, or even better, QoE, compared to the state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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MLLM CAN SEE? DYNAMIC CORRECTION DECODING FOR HALLUCINATION MITIGATION

arXiv

引用

arXiv 2024年

作者： Wang, Chenxi Chen, Xiang Zhang, Ningyu Tian, Bozhong Xu, Haoming Deng, Shumin Chen, Huajun Zhejiang University China Nanjing University of Aeronautics and Astronautics China National University of Singapore NUS-NCS Joint Lab Singapore Zhejiang Key Laboratory of Big Data Intelligent Computing China

Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the objects in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language model suppressing the visual information, leading to hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MLLMs (DeCo), which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits. Note that DeCo is model agnostic and can be seamlessly incorporated with various classic decoding strategies and applied to different MLLMs. We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines, highlighting its potential to mitigate hallucinations1 Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Toward Compact and Robust Model Learning Under Dynamically Perturbed Environments

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第6期34卷 4857-4873页

作者： Luo, Hui Zhuang, Zhuangwei Li, Yuanqing Tan, Mingkui Chen, Cen Zhang, Jianlin Chinese Acad Sci Inst Opt & Elect Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Key Lab Opt Engn Chengdu 610209 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China South China Univ Technol Sch Software Engn Minist Educ Guangzhou 510641 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510641 Peoples R China South China Univ Technol Sch Future Technol Guangzhou 510641 Peoples R China

Network pruning has been widely studied to reduce the complexity of deep neural networks (DNNs) and hence speed up their inference. Unfortunately, most existing pruning methods ignore the changes in the model's robustness before and after pruning, which makes pruned models vulnerable under dynamically perturbed environments (e.g., autonomous driving). Only a few works have explored the robustness of pruned models against adversarial attacks that significantly differ from perturbations in real-world scenarios. To bridge the gap between real-world applications and existing studies, in this work, we propose an adversarial pruning scheme, which automatically identifies and preserves robust channels to obtain robust pruned models that are suitable for practical deployment in dynamically perturbed environments. Specifically, to simulate real-world perturbations, we first employ multi-type adversarial attack samples and adversarial perturbation samples generated by an adversarial perturbation generator to create mixed noise samples. Then, we propose a plug-and-play feature scoring module and a novel contribution difference loss to evaluate the robustness of intermediate features dynamically. Next, to leverage robust intermediate features to identify robust channels, we have developed a simple but effective gating mechanism that evaluates the robustness of channels and preserves robust channels during training. Lastly, we compress the model in a layer-wise or block-wise manner. Compared to existing methods, our scheme enhances the robustness of the pruned model in a broader sense, making it better able to against dynamic perturbations in the real world. Extensive experimental results on well-known dataset benchmarks and popular network architectures demonstrate the effectiveness of our method.

关键词： Robustness data models Perturbation methods Training Computational modeling Predictive models Pipelines Network pruning robustness dynamically perturbed environments intermediate features adversarial pruning

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A Transformer Autoencoder with Hidden Layer Extension for Multivariate Time Series Anomaly Detection

SSRN

引用

SSRN 2023年

作者： Zhang, Xiaoxia Chen, Yi Hu, Feng Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

With the development of smart industry and connection technology, massive amounts of multivariate time series data are generated by various industrial systems, including air quality monitoring, power plant, and the stock industry, among others. These data often contain anomalies, making it essential to perform anomaly detection on multivariate time series data using both supervised and unsupervised methods. However, anomalies are typically rare and uncertain, current supervised methods cannot identify unknown anomalies, and labeling anomalies is still a significant challenge. Additionally, existing unsupervised autoencoder methods cannot learn rich representations in the hidden layer of the model, which results in inadequate detection accuracy. Furthermore, real-world systems often require prompt anomaly detection and detailed reasons for the occurrence. To address these issues, we propose a Transformer Autoencoder with Hidden Layer Extension for Multivariate Time Series Anomaly Detection through fusing quantitative prior knowledge to enhance hidden layers, short for HLEAD, by employing which can improve the efficiency of anomaly detection and explore the root cause of anomalies. Extensive experiments are conducted based on six large datasets demonstrating our method HLEAD can outperform other methods on accuracy, robustness, and detection speed. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

引用

SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Research on Crop Disease and Pest Surveillance based on big data and Image Processing Technologies

Research on Crop Disease and Pest Surveillance based on Big ...

引用

Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Yimei Yang Yujun Yang Huixia Shu Changhua Zhao Shaowei Deng College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

Recently, with the increasing complexity of agricultural systems and climate change, agricultural pest and disease surveillance and early detection have become critical tasks for guaranteeing food stability and promoting sustainable farming growth. Agricultural pest and disease surveillance is a key component of agricultural production, directly affecting crop yield and quality. As big data advances and image processing technologies rapidly, their application in crop pest and disease surveillance has become increasingly widespread. By integrating multi-source data, such as remote sensing data, meteorological data, ecological data, and pest and disease survey data, researchers can build more accurate and efficient pest and disease surveillance models. This paper reviews the research progress of big data and image processing technologies in crop pest and disease surveillance, analyzes the advantages and challenges of current technologies, and proposes future research directions to enhance the automation and intelligence of systems. Additionally, it discusses the integration of deep learning and other techniques to achieve efficient information mining and accurate retrieval of disaster-related data, aiming to provide scientific guidance and technical support for agricultural production.

关键词： Crops Plant diseases Climate change Pest control Agriculture big data Surveillance Image processing

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

引用

8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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