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

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

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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

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Identification of Virus-Receptor Interactions Based on Network Enhancement and Similarity 16th

Identification of Virus-Receptor Interactions Based on Netwo...

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16th International Symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Zhu, Lingzhi Yan, Cheng Duan, Guihua Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China School of Computer and Information Science Hunan Institute of Technology Hengyang421008 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China

ISBN: (纸本)9783030578206

As a main composition of the human-associated microbiome, viruses are directly associated with our health and disease. The receptor-binding is critical for the virus infection. So identifying potential virus-receptor interactions will help systematically understand the mechanisms of virus-receptor interactions and effectively treat infectious diseases caused by viruses. Several computational models have been developed to identify virus-receptor interactions based on assumption that similar viruses show similar interaction patterns with receptors and vice versa, but the performance need to be improved. Furthermore, the virus network and the receptor network are also noisy. Therefore, we present a new prediction model (NERLS) to identify potential virus-receptor interactions based on Network Enhancement, virus sequence information and receptor sequence information by Regularized Least Squares. Firstly, the virus network is constructed based on the virus sequence similarity and Gaussian interaction profile (GIP) kernel similarity of viruses by a mean method. They are calculated based on the viral RefSeq genomes downloaded from NCBI and known virus-receptor interactions, respectively. Similarly, we also use the same mean method to construct the receptor network based on the amino acid sequence similarity and known virus-receptor interactions. Then Network Enhancement is applied to denoise the virus network and the receptor network. Finally, we employ the regularized least squares algorithm to identify potential virus-receptor interactions. The 10-fold cross validation (10CV) experimental results indicate that an average Area Under Curve (AUC) values of NERLS is 0.8930, which is superior to other computing models of 0.8675 (IILLS), 0.7959 (BRWH), 0.7577 (LapRLS), and 0.7128 (CMF). Furthermore, the Leave One Out Cross Validation (LOOCV) experimental results also show that NERLS can achieve the AUC values of 0.9210, which is better than other models (IILLS: 0.9061, BRWH: 0

关键词： Viruses

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MTFIL-Net: automated Alzheimer’s disease detection and MMSE score prediction based on feature interactive learning

MTFIL-Net: automated Alzheimer’s disease detection and MMSE...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jin Liu Xu Tian Jianxin Wang Rui Guo Hulin Kuang Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Automatic detection of Alzheimer’s disease (AD) is conducive to intervention in the disease progression. MMSE score prediction can reveal the development of AD. In recent years, some studies have designed multi-task methods for AD detection and MMSE score prediction to take advantage of the correlation between them. However, how to use the correlation between the two task features is still a problem. To address this challenge, we propose a multi-task feature interactive leanrning network (MTFIL-Net) to perform AD detection and MMSE score prediction. First, we interact the features acquired by CNNs corresponding to the two tasks to take advantage of the feature correlation between the two tasks. The interaction module extracts the shared features of the two tasks and concatenate them with the features of the two task. Then, we design a joint loss based on cross entropy and smooth L1 function. We use the distribution of MMSE scores to dynamically adjust the relationship between the two tasks. We validate our method with subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We use the ADNI1 dataset for training and testing, and used the ADNI2 dataset as an external validation set. Our proposed MTFIL-Net reached an ACC of 0.86 for AD detection and a correlation coefficient of 0.67 for MMSE score prediction on the ADNI1 dataset, and reached an ACC of 0.85 for AD detection and a correlation coefficient of 0.66 for MMSE score prediction on the ADNI2 dataset. Experiment results show that MTFIL-Net effectively utilizes the correlation between AD and MMSE score.

关键词： Correlation coefficient Training Neuroimaging Correlation Feature extraction Multitasking Task analysis

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Collaborative Edge Service Placement for Maximizing QoS with Distributed Data Cleaning 31

Collaborative Edge Service Placement for Maximizing QoS with...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Liang, Yuzhu Wang, Wenhua Zheng, Xi Liu, Qin Wang, Liang Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Computing Macquarie University NSW2109 Australia College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519000 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519000 China

ISBN: (纸本)9798350399738

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines. © 2023 IEEE.

关键词： Edge computing

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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De novo Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization 1

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16th International Symposium on bioinformatics Research and Applications, ISBRA 2020

作者： Wu, Gaoyan Yang, Mengyun Li, Yaohang Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan Shaoyang University Shaoyang422000 China Department of Computer Science Old Dominion University NorfolkVA23529 United States

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

The identification of drug-target interactions plays a crucial role in drug discovery and design. However, capturing interactions between drugs and targets via traditional biochemical experiments is an extremely laborious, expensive and time-consuming procedure. Therefore, the use of computational methods for predicting potential interactions to guide the experimental verification has attracted a lot of attention. In this paper, we propose a new algorithm, named Laplacian Regularized Schatten-p Norm Minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets. First, we take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten-p norm minimization model to improve prediction performance in the new drug/target cases by combining the loss function with a Laplacian regularization term. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers (ADMM) algorithm. Performance evaluations on benchmark datasets show that LRSpNM achieves better and more robust performance than five state-of-the-art drug-target interaction prediction algorithms. In addition, we conduct case study in practical applications, which also illustrates the effectiveness of our proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Temporal Group Attention Network with Affective Complementary Learning for Gait Emotion Recognition

Temporal Group Attention Network with Affective Complementar...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ziyi Zhu C. L. Philip Chen Haiqi Liu Tong Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Pazhou Lab Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods.

关键词： Emotion recognition Noise reduction Noise Skeleton bioinformatics

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SCOTCluster: Deep Clustering with Optimal Transport for Large-scale Single-cell RNA-seq Data

SCOTCluster: Deep Clustering with Optimal Transport for Larg...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Faning Long Xiaojun Ding Xiaoqing Peng Jianxin Wang Xiaoshu Zhu School of Computer Science and Engineering Yulin Normal University Yulin China Center for Medical Genetics School of Life Sciences Central South University Changsha China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Single-cell RNA sequencing (scRNA-seq) presents cell heterogeneity in a high resolution to explore cell development. The high dimension and the high noise in scRNAseq data bring some computational challenges. By introducing optimal transmission regularization, we proposed a novel deep clustering method, called SCOTCluster, which accurately learned low-dimensional representations. In SCOTCluster, a joint training strategy was designed by integrating AutoEncoder and soft k-means. Notably, to improve simultaneously the accuracy and robustness, the optimal transmission was introduced in the objective function of soft k-means, and entropy regularization and Sinkhorn iterative algorithm were performed to constraint the cluster size. To test the performance, we compared SCOTCluster with five state-of the-art methods on 16 real large-scale scRNA-seq datasets. The experimental results showed that SCOTCluster improved the training stability and clustering performance.

关键词： Training Sequential analysis RNA Conferences Clustering methods Linear programming Robustness

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Research on personalized learning path management based on blockchain technology 16

Research on personalized learning path management based on b...

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16th IEEE International Conference on computer science and Education, ICCSE 2021

作者： Changhua, Zhao Chunqiao, Mi Huaihua University School of Computer Science and Engineering Hunan Huaihua418000 China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua18000 China

ISBN: (纸本)9781665414685

Aiming at the problem that learners' personal information, courses and relevant certificates can not provide detailed information about learning depth and learning or teaching methods, this paper takes learners' learning information, learning record smart contract and Ethereum Blockchain related technology as the research object, aiming to use Ethereum Blockchain and smart contract to connect learners' learning records. On the one hand, it can use the block data stored in the Blockchain to easily verify the authenticity of the data and ensure the privacy and security of the data. On the other hand, it can provide personalized learning path for learners and be used to evaluate a person's educational achievement, employment suitability and intelligence evaluation. © 2021 IEEE.

关键词： Smart contract

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Identification of Disease-Associated Genes Based on Differential Intron Retention

Identification of Disease-Associated Genes Based on Differen...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Zhenpeng Wu Jiantao Zheng Hong-Dong Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Differential analysis of gene-level expression is a commonly used approach for identifying disease-associated genes. Recently, intron retention (IR) has been shown to be associated with complex diseases such as cancers. IR provides value that is complementary to traditional gene-level expression. However, a systematic method to exploit IR for identifying disease-associated genes remains largely unexplored. We developed a pipeline to identify the disease-associated gene based on differential intron retention (IRDAG), which integrates IR events detected by two methods, IRFinder and iREAD. We applied it to Alzheimer's disease (AD). We found that many of the differential genes detected based on IR were not able to be discovered by the traditional gene-level differential expression method, suggesting that our method is complementary to traditional methods. We showed that the differential genes identified with IRDAG were functionally related to AD based on the analysis of protein-protein interaction networks and brain-specific functional gene networks. Being complementary to the existing method, IRDAG provides a new and generic approach for identifying the disease-associated gene.

关键词： Diseases Pipelines Proteins Splicing Databases Testing bioinformatics

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