检索结果-内蒙古大学图书馆

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Min Zeng Nian Wang Yifan Wu Yiming Li Fang-Xiang Wu Min Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9781665429825

Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://***/CSUBioGroup/EP-GBDT.

关键词： Proteins Protein engineering Costs Codes Conferences Predictive models Biomedical measurement

来源：评论

学校读者我要写书评

暂无评论

FEDGCA: GLOBAL CONSISTENT AUGMENTATION BASED SINGLE-SOURCE FEDERATED DOMAIN GENERALIZATION

arXiv

引用

arXiv 2024年

作者： Liu, Yuan Wang, Shu Qu, Zhe Li, Xingyu Kan, Shichao Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha China Tulane University New Orleans United States

Federated Domain Generalization (FedDG) aims to train the global model for generalization ability to unseen domains with multi-domain training samples. However, clients in federated learning networks are often confined to a single, non-IID domain due to inherent sampling and temporal limitations. The lack of cross-domain interaction and the in-domain divergence impede the learning of domain-common features and limit the effectiveness of existing FedDG, referred to as the single-source FedDG (sFedDG) problem. To address this, we introduce the Federated Global Consistent Augmentation (FedGCA) method, which incorporates a style-complement module to augment data samples with diverse domain styles. To ensure the effective integration of augmented samples, FedGCA employs both global guided semantic consistency and class consistency, mitigating inconsistencies from local semantics within individual clients and classes across multiple clients. The conducted extensive experiments demonstrate the superiority of *** Codes I.2 Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization

FedGCA: Global Consistent Augmentation Based Single-Source F...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuan Liu Shu Wang Zhe Qu Xingyu Li Shichao Kan Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Tulane University New Orleans USA

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

关键词： Training Federated learning Semantics

来源：评论

学校读者我要写书评

暂无评论

Joint Learning of Primary and Secondary labels based on Multi-scale Representation for Alzheimer’s Disease Diagnosis

Joint Learning of Primary and Secondary Labels based on Mult...

引用

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hong-Dong Li Rui Guo Junjian Li Jianxin Wang Yi Pan Jin Liu Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Georgia State University Atlanta USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

The cause of Alzheimer's disease (AD) is insufficient to understand so far, and its diagnosis is challenging in clinical practice. Recently, the convolutional neural network (CNN) model has shown impressive performance in medical image analysis. Combining CNN with magnetic resonance imaging (MRI) image has excellent potential for AD diagnosis. However, it is still a challenging task. To address the challenge, we propose a joint learning method based on multi-scale representation (JL-MSR). The multi-scale representation is proposed to obtain more feature maps by the multi-scale atrous convolutions. Furthermore, in order to use the intrinsic relationship between diagnostic results and clinical scores, we propose a joint learning strategy using the diagnosis result as the primary label and the Mini-Mental State Examination (MMSE) score as the secondary label to joint training. The proposed method is evaluated on a dataset of 417 subjects (including 188 AD and 229 health controls (HC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method achieves an accuracy of 88.1% and an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.942 for AD diagnosis, respectively. Compared with a state-of-the-art method in AD diagnosis, our proposed method performs better, and has potential in clinical diagnosis.

关键词： Magnetic resonance imaging Convolution Diseases Three-dimensional displays Clinical diagnosis Alzheimer's disease Tools

来源：评论

学校读者我要写书评

暂无评论

Joint QoS Prediction for Web Services based on Deep Fusion of Features

引用

IEEE Transactions on Network and Service Management 2023年 1-1页

作者： Ding, Linghang Liu, Jianxun Kang, Guosheng Xiao, Yong Cao, Buqing Hunan Provincial Key Lab. for Services Computing and Novel Software Technology and School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE). IEEE

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Faster Parameterized Algorithm for Bipartite 1-Sided Vertex Explosion 16th

A Faster Parameterized Algorithm for Bipartite 1-Sided Vert...

引用

16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Liu, Yunlong Xiao, Guang Liu, Ao Wu, Di Huang, Jingui College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Xiangtan University Xiangtan411101 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9783031496134

Given a bipartite graph G = (T∪ B, E), the problem bipartite 1-sided vertex explosion is to decide whether there exists a planar 2-layer embedding of G after exploding at most k vertices of B. For this problem, which is known to be NP-complete, parameterized algorithms have received increasing attention more recently. In this paper, we focus on the problem parameterized by the number k of allowed exploded vertices of B and develop a faster algorithm for it. More specifically, we show that this parameterized problem admits a kernel of at most 10.5k vertices, and present a fixed-parameter tractable algorithm running in time O(2. 31 k· m), where m is the number of edges of G. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Improving the Prediction of Disease-associated Genes by Integrating Annotated Gene Sets

Improving the Prediction of Disease-associated Genes by Inte...

引用

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Chao Deng Cui-Xiang Lin Hong-Dong Li Human Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Identifying disease-associated genes is key to studying the pathogenic mechanism of complex diseases. Existing methods for predicting disease genes are dominantly based on molecular networks or omics data, including gene expression, protein expression, co-expression networks, protein-protein interaction, etc. The annotated gene sets such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways represent knowledge about the functional association between genes, which may be of predictive value for disease gene identification. However, the knowledge is barely explored in existing approaches. In this study, we propose a new method for predicting disease-associated genes by integrating annotated gene sets. It first constructs a signal matrix by integrating annotated gene sets in the MSigDB data, a comprehensive database of curated gene sets. Then it uses the signal matrix as input features to build a prediction model with machine learning approaches. We compared our method with existing disease gene prediction methods on five complex diseases. The results showed that our method is superior to other methods.

关键词： Proteins Genomics Machine learning Predictive models Ontologies Prediction algorithms Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

An Alzheimer’s disease gene prediction method based on ensemble of genome-wide association study summary statistics

An Alzheimer’s disease gene prediction method based on ense...

引用

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jia-Hao Song Cui-Xiang Lin Hong-Dong Li School of Computer Science and Engineering Human Provincial Key Lab on Bioinformatics Central South University Changsha China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The hitherto unknown specific etiology of Alzheimer’s disease (AD) poses a challenge for its prevention, diagnosis and treatment. Although genome-wide association studies (GWAS) are currently making rapid progress in identifying genetic variants associated with AD, the pathogenic mechanisms of the genetic loci identified are largely unknown. Transcriptome-wide association studies (TWAS) are an important class of methods for predicting disease genes. TWAS can explore the association of genes with the disease in relevant tissues by integrating genome-wide genetic regulatory data from specific tissues and disease-associated GWAS summary statistics. We found that TWAS analysis using different GWAS summary statistics may produce inconsistent results. To address this issue, we used ensemble summary statistics for AD-associated gene prediction considering the complementary nature of different datasets and the comparative nature between the results generated from different datasets. The prediction results were compared and analyzed to identify AD associated genes. The predicted genes were validated. In case study of an individual genes, we identified a potential association between AZGP1 and AD disease by this method.

关键词： Pathology Genomics Prediction methods bioinformatics Alzheimer's disease Diseases

来源：评论

学校读者我要写书评

暂无评论

One-Step Synthesis of Tungsten-Doped Carbonated Polymer Dots with High Sensitivity to Fe (Iii) and Ph Environments

SSRN

引用

SSRN 2022年

作者： Han, Yushu Bao, Rui Yi, Jianhong Li, Hongdong Liu, Liang Huang, Shuyu Li, Zhaojie Zhang, Wenfu Min, Deqi Faculty of Material Science and Engineering Kunming University of Science and Technology Kunming650093 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha410083 China

Carbonized polymer dots (CPDs) have drawn a lot of attention in the past ten years because of their excellent selectivity and sensitivity. In this study, tungsten was doped into CPDs (W-CPDs) using a simple hydrothermal technique for the first time. With an excitation/emission maximum of 365/455 nm and excitation-independent emission, W-CPDs exhibit blue fluorescence and have an average particle diameter of 2.33 nm. Because of the dynamic quenching between Fe (III) and W-CPDs, there is a good linear relationship between the Fe (III) concentration and the limit of detection (LOD) of 0.012 μM for W-CPD fluorescence intensity. The intensity of the W-CPDs fluorescence is also found to be linearly correlated with pH values between 5.0 and 10.0. This technique enables quick detection of Fe (III) in drinking water and is expected to lower the risk of Fe (III) ingestion by humans. In addition, the creation of fluorescent flexible devices and information encryption are fascinating applications for W-CPDs. © 2022, The Authors. All rights reserved.

关键词： Potable water

来源：评论

学校读者我要写书评

暂无评论

One-Step Synthesis of Tungsten-Doped Carbonated Polymer Dots with High Sensitivity to Fe (Iii) and Ph Environments

SSRN

引用

SSRN 2022年

关键词： Potable water

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：