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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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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

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

ISBN: (纸本)9781665401265

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. © 2021 IEEE.

关键词： Gene Ontology

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Prediction of virus-receptor interactions based on multi-view learning and link prediction

Prediction of virus-receptor interactions based on multi-vie...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Lingzhi Zhu Kai Zheng Guihua Duan Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China School of Computer Science and Engineering Hunan Institute of Technology Hengyang Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Receptor-binding is the first step of viral infection. Discovering potential virus-receptor interactions may give insight into potential strategies for treating viral infectious diseases. Most of computational methods for the virus-receptor interaction prediction are mainly based on sequence information. They neither makes effective use of structure information nor effectively handles with missing values of multiple similarities. In addition, the Link Prediction via linear optimization (LP) only uses contribution of neighbors of a node and ignores contribution of neighbors of another node on the network link. In this article, we present a virus-receptor interaction prediction method (MVLP) based on Multi-View learning and LP via contributions of all neighbors of two nodes on the network link. First, missing values of the receptor secondary structure similarity, the receptor conserved domain secondary structure similarity, the viral protein secondary structure similarity, the viral protein sequence similarity and the viral genome sequence similarity are updated by the gaussian radial basis function (GRB). To improve these similarities, we fuse updated and initial values of each similarity with multi-view learning, respectively. Next, three virus values and receptor similarities are integrated into the comprehensive virus and receptor similarity by the averaging method, respectively. Finally, LP based on contribution of neighbors of two nodes is presented for the virus-receptor interaction prediction. To evaluate the ability of MVLP, we compare MVLP with four related methods in 10 fold Cross-Validation (10CV). Computational results indicate that an average Area Under Curve (AUC) values of MVLP on viralReceptor sup and viralReceptor are 0.9427 and 0.9444, respectively, which are superior to other related methods. Furthermore, a case study also demonstrates the ability of MVLP in practice.

关键词： Protein engineering computer viruses Infectious diseases Fuses Genomics Prediction methods Protein sequence

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation

Multi-view Hypergraph-based Self-supervised Learning Model f...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jiexun Shen Dongfan Li Wanjun Chen Yong Xiao Guosheng Kang Jianxun Liu Zhenlian Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines.

关键词： Knowledge engineering Mashups Noise Neural networks Collaboration Service computing Self-supervised learning Feature extraction Data models Noise measurement

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Interactive Web API Recommendation via Exploring Mashup-API Interactions and Functional Description

Interactive Web API Recommendation via Exploring Mashup-API ...

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International Conference on computer Supported Cooperative Work in Design

作者： Wanjun Chen Qiqi Chen Jiexun Shen Guosheng Kang Jianxun Liu Buqing Cao Lihong Zhang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the advance of service computing technology, the number of Web APIs has risen dramatically over the Internet. Users tend to use Web APIs to achieve their business needs. However, it is difficult for users to find and select the desirable ones due to the plethora of Web APIs. To address this problem, some collaborative filtering-based Web API recommendation methods have been proposed even though their performance is still far from satisfaction, since they only rely on Mashup-API interactions and feature interactions are not considered in the recommendation model. To further improve the recommendation performance, this paper proposes an interactive Web API recommendation method via exploring both Mashup-API interactions and functional description documents of Mashups and Web APIs. Specifically, LightGCN is employed to derive the node representations for the Mashup-API interaction graph, and BERT model is used for the text representations of functional description documents. Furthermore, the two presentations of both the Mashup and Web API are concatenated as the input of ANFM (Attentional Neural Factorization Machine) model, in which low and high-order feature interactions are fully modeled and the weights of feature interactions are trained by attention mechanism. Solid experiments are conducted over a real-world dataset and the experimental results indicate that the proposed method outperforms the baseline methods.

关键词： Attention mechanisms Federated learning Mashups Computational modeling Semantics Neural networks Service computing

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JMF-SS: Joint Matrix Factorization for Web API Recommendation with Mashup-Mashup Similarities and API-API Similarities

JMF-SS: Joint Matrix Factorization for Web API Recommendatio...

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International Conference on computer Supported Cooperative Work in Design

作者： Lihong Zhang Jing He Yamei Nie Xiaokang Zhou Qiqi Chen Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the development of service computing technology, the number of publicly available Web APIs online has increased dramatically. Developers tend to use Web APIs to implement their software development requirements. However, due to the large number of Web APIs, how to select the appropriate Web API from the huge resource library for Mashup development has become a challenge. To solve this problem, some researchers have proposed Web API recommendation methods based on collaborative filtering or matrix factorization. However, the Web API recommendation performance is still limited due to the reliance on Mashup- API interaction and the ignorance of Mashup and Web API description documents. Moreover, the Mashup-API interaction matrix is extremely sparse, resulting in low accuracy in matrix factorization and collaborative filtering. To further improve the recommendation performance, we propose a novel joint matrix factorization method for the Mashup-API interaction matrix by incorporating the Mashup-Mashup similarity matrix and the API-API similarity matrix. A set of experiments are conducted on a real-world dataset, and the experimental results show that the proposed method outperforms the baselines.

关键词： Measurement Accuracy Federated learning Mashups Collaborative filtering Service computing Libraries

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A General Complementary API Recommendation Framework based on Learning Model

A General Complementary API Recommendation Framework based o...

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IEEE International Conference on Web Services (ICWS)

作者： Qiqi Chen Guosheng Kang Yamei Nie Jianxun Liu Buqing Cao HNUST Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great challenge for users. Various Web API recommendation methods have been proposed to address this issue, aiming to simplify the complex selection process. Despite these efforts, limited studies have been conducted on complementary function recommendation. In this context, a general complementary Web API recommendation framework based on a learning model, named CoWAR, is designed to recommend complementary Web APIs tailored for Mashup creation, based on the user’s selected Web APIs. Specifically, we propose a data labeling algorithm to generate the labeled dataset based on Mashup-API interactions derived from historical Mashups and Web APIs. Additionally, we employ BERT model to generate representation vectors of Web APIs based on the functionality description documents. Subsequently, we utilize SANFM (Self-Attentional Neural Factorization Machines) to train the complementary Web API recommendation model with the labeled sample dataset based on representation vectors of Web APIs. To the best of our knowledge, this is the first work addressing the complementary function recommendation problem with a learning model. By conducting a set of experiments over a real-world dataset, the effectiveness of the proposed approach is validated. The experimental results demonstrate that the learning model outperforms the traditional machine learning-based models and several deep learning-based models.

关键词： Training Measurement Mashups Service computing Vectors Data models labeling Context modeling

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Online Course Recommendation by Exploring User Interaction and Course Description

Online Course Recommendation by Exploring User Interaction a...

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International Conference on computer Supported Cooperative Work in Design

作者： Weike Zhou Xiaokang Zhou Jing He Yamei Nie Qiqi Chen Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the wide use of Massive Open Online Courses (MOOCs) and online education, the large number and variety of educational resources on the online course open platform make it difficult for users to choose, and the requirements for course recommendation algorithms and model performance are getting higher and higher. The existing course recommendation algorithms often ignore the interactive information between n learners and courses and the course descriptions, resulting in poor recommendation performance. To solve the problems above, our work proposes an online course recommendation approach via exploring user interactions and course description documents. Specifically, LightGCN model is used to represent the user-course interaction graph to derive the structural embeddings of users and courses. Moreover, BERT model is used to represent the course description documents to derive the description embeddings of courses. Based on the three embeddings, ANFM (Attention Neural Factorization Machine) model is applied to perform online course recommendation by modeling low-order and high-order feature interactions, and learning the weights of the feature interactions through the attention mechanism. Finally, solid experiments are conducted over a real-world public dataset. Experimental results show that the proposed approach outperforms the baseline methods under NDCG, Recall and Precision.

关键词： Electronic learning Attention mechanisms Federated learning Education Solids

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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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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

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

ISBN: (纸本)9781665401265

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. © 2021 IEEE.

关键词： Forecasting

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A Fully Automated CT-Guided Learning for Survival Prediction of Esophageal Cancer

A Fully Automated CT-Guided Learning for Survival Prediction...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hailin Yue Jin Liu Hulin Kuang Jianhong Cheng Junjian Li Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

Accurately predicting survival of esophageal cancer is essential for clinical precision treatment. However, the existing region of interest (ROI) based methods not only require prior medical knowledge to complete the delineation of tumor, but may also lead to excessive sensitivity of the model towards ROI. To address these challenges, we design a fully automated CT-guided learning that combines a CNN-Transformer size aware U-Net and a ranked survival prediction network together to automatically predict the survival of patients with esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism into the base of the encoder in the U-Net to capture the long-range dependency in the 3D CT images. Then, to alleviate the imbalance between the ROI and the background in CT images, we design a size-aware coefficient for the segmentation loss. Finally, we design a ranked pair sorting loss to learn more fully the ranked information hidden in esophageal cancer patients. To validate the effectiveness of our method, we conduct extensive experiments on a dataset containing 759 esophageal cancer samples. The experimental results demonstrate that our proposed method can still achieve the best performance in survival prediction without ROI ground truth.

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M3CI-Net: Multi-Modal MRI-Based Characteristics Inspired Network for IDH Genotyping

M3CI-Net: Multi-Modal MRI-Based Characteristics Inspired Net...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jingxiao Yao Jin Liu Jianhong Cheng Hulin Kuang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

Isocitrate dehydrogenase (IDH) is a key molecular feature for gliomas, and the prediction of IDH is also an important task for computer-aided diagnosis using magnetic resonance imaging (MRI). To address this changllenge, we introduce a multi-modal MRI-based characteristics inspired network for IDH Genotyping (M 3 CI-Net), which pay more attention to the different characteristics information of different MRI modalities T1, T2, T1ce, Flair. In M 3 CI-Net, a pre-fusion module with multi-channel attention mechanism is used to fuse T1ce and Flair modalities and capture as much as possible luminance and contrast information, and the edge information is obtained from T2 modality by using edge detection module. Finally, the feature information between modalities are fused and input into a CNN-Transformer based encoder structure to extract shared spatial and global information from multi-modal MRI, and the information of multiple scales frome encoder are input into the linear layer for IDH genotype classification after pooling, meanwhile, the CNN based decoder with skip-connection for glioma segmentation works for assisting IDH genotyping. Then, we proposed images’ pre-fusion loss, segmentation loss, IDH genotyping loss, and use uncertainty weight training method to balance the weights of these loss. we evaluate our proposed method on Brats2020, and achieve an acceracy of 0.88, an AUC of 0.94, a specificity of 0.92, a sensitivity of 0.84 in IDH genotyping, which is superior to the state-of-the-art methods.

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