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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

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

ISBN: (纸本)9798350329223

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process. © 2023 IEEE.

关键词： Web services

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ViTRMSE: a three-dimensional RMSE scoring method for protein-ligand docking models based on Vision Transformer

ViTRMSE: a three-dimensional RMSE scoring method for protein...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Linyuan Guo Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Protein-ligand interactions (PLIs) play important roles in cellular activities and drug discovery. Due to the technical difficulty and high cost of experimental methods, there is considerable interest in the development of computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most important and difficult aspects of protein-ligand docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. We present a new deep learning-based scoring function for ranking protein-ligand docking models based on Vision Transformer(ViT), named ViTRMSE. To recognize near-native conformations from a set of decoys, ViTRMSE voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. Benefiting from the Vision Transformer architecture, ViTRMSE can effectively capture the subtle differences between spatially and energetically favorable near-native conformations and unfavorable non-native decoys without needing extra information. ViTRMSE is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power.

关键词： Proteins Solid modeling Protein engineering Three-dimensional displays Shape Predictive models Network architecture

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MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction

arXiv

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arXiv 2024年

作者： Jiang, Han Duan, Junwen Qu, Zhe Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha China

Unsupervised rationale extraction aims to extract text snippets to support model predictions without explicit rationale annotation. Researchers have made many efforts to solve this task. Previous works often encode each aspect independently, which may limit their ability to capture meaningful internal correlations between aspects. While there has been significant work on mitigating spurious correlations, our approach focuses on leveraging the beneficial internal correlations to improve multi-aspect rationale extraction. In this paper, we propose a Multi-Aspect Rationale Extractor (MARE) to explain and predict multiple aspects simultaneously. Concretely, we propose a Multi-Aspect Multi-Head Attention (MAMHA) mechanism based on hard deletion to encode multiple text chunks simultaneously. Furthermore, multiple special tokens are prepended in front of the text with each corresponding to one certain aspect. Finally, multi-task training is deployed to reduce the training overhead. Experimental results on two unsupervised rationale extraction benchmarks show that MARE achieves state-of-the-art performance. Ablation studies further demonstrate the effectiveness of our method. Our codes have been available at https://***/CSU-NLP-Group/MARE. Copyright © 2024, The Authors. All rights reserved.

关键词： Text mining

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BEA-SegNet: Body and Edge Aware Network for Medical Image Segmentation

BEA-SegNet: Body and Edge Aware Network for Medical Image Se...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hulin Kuang Yixiong Liang Ning Liu Jin Liu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (纸本)9781665429825

Medical image segmentation is a fundamental step for diagnosis and prognosis. This study proposes a new body and edge aware network for automated 2D medical image segmentation (called BEA-SegNet). The proposed BEA-SegNet consists of a shared encoder, a body and edge decouple (BEdecouple) module, two parallel decoders for body and edge segmentation. In the encoder and decoders, short-term multi-scale concatenation (STMSC) modules are utilized to implement multi-scale representation. We design a BEdecouple module to decouple the convolutional features into the body and edge features, making the proposed method be body and edge aware. The body and edge decoders utilize Bedecouple modules in each level to learn more effective features for the body and edge segmentation respectively, and their outputs are fused to generate the final segmentation. Besides, the body and edge supervision are applied to improve the final segmentation. The proposed BEA-SegNet is trained and evaluated on the International Skin Imaging Collaboration challenge 2018 dataset (ISIC2018). Experimental results show that the proposed BEA-SegNet achieves an average Dice similarity coefficient of 90.3% and an average Hausdorff distance of 15.9 for the skin lesion segmentation task and outperforms five benchmarks for skin lesion segmentation.

关键词： Image segmentation Image edge detection Conferences Benchmark testing Skin Decoding Lesions

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KL-RF: Predicting disease-gene associations with model fusion

KL-RF: Predicting disease-gene associations with model fusio...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hai Liu Hong-Dong Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University ChangSha Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Appropriate disease gene prediction methods are not only critical for the diagnosis of many genotypic diseases, but well-positioned to help scientists discover the links between complex diseases and disease-causing genes. As the development of high-throughput biotechnology, a great quantity of biomolecular interaction networks and omics data emerge rapidly. Because of the capacity of biomolecular networks to indicate complex interactions between elements in disease, they are one of the most frequently used data in the field of disease-gene associations prediction. Fusing these biomolecular networks effectively is hopeful to improve the performance of computational methods. Here, we develop a method to predict the associations between disease and gene by fusing the information of heterogeneous datasets. This method first learns the latent feature representation from disease-gene similarity networks via graph representation learning methods, then utilizes a Beyesian approach to obtain the posterior distribution of the feature matrix, and subsequently fuses the posterior distribution based on KL divergence and predicts the associations by random forest (KL-RF) finally. We compared KL-RF with several classic methods, and the results demonstrate that it performs better than the existing methods. Further analysis of the top-10 unknown disease genes and case study also reveals the ability of the proposed method. The source codes is available on https://***/genemine/KL-RF.

关键词： Representation learning Proteins Biological system modeling Source coding Predictive models Prediction algorithms Data models

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ASNet: An Adversarial Sparse Network for Multi-task Biomedical Named Entity Recognition

ASNet: An Adversarial Sparse Network for Multi-task Biomedic...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Junwen Duan Huai Guo Min Zeng Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Biomedical named entity recognition (BioNER) is to extract entities, such as genes and proteins, from biomedical texts, where there is often a lack of high-quality training data. Recent work addresses this issue by multi-task learning with multiple datasets. However, these methods are usually over-parameterized, and some even suffer from negative transfer issues. To address above problems, we propose adversarial sparse sharing mechanism, which trains a sparsely shared encoder on multiple tasks with both task-agnostic and task-specific subnetworks. With adversarial training, we guide the task-agnostic subnetwork to learn shared task-invariant features and the task-specific subnetwork to learn task-dependent features. For a particular task, only the shared and its subnetwork are activated, which greatly reduces the number of parameters and avoids interference among tasks. Experimental results on 15 benchmark BioNER datasets show that our proposed method outperforms or is competitive with baseline methods with fewer parameters. Our code is released at: https://***/CSU-NLP-Group/ASNet

关键词： Training Proteins Codes Text recognition Biological system modeling Training data Interference

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ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention

ARSC-Net: Adventitious Respiratory Sound Classification Netw...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Lei Xu Jianhong Cheng Jin Liu Hulin Kuang Fan Wu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (纸本)9781665429825

Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), which combines residual block with channel-spatial attention for accurate classification. Specifically, we extract two types of features from adventitious respiratory sound, including Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-spectrogram. The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task. Moreover, the channel-spatial attention can enhance the feature representation, in which the channel attention explores the inter-channel relationship of the spectrums, and then the inter-spatial correlation mapping is generated by the spatial attention introduced serially. We evaluate our proposed method on ICBHI 2017 database. Experimental results show that our proposed method achieves encouraging predictive performance with an accuracy of 80.0% for identifying abnormal sounds from normal sounds, and with an accuracy of 92.4% for distinguishing crackles from wheezes. In addition, our method also achieves a score of 56.76% for the four-class sound classification of adventitious sounds and outperforms several state-of-the-art methods.

关键词： Correlation Databases Fuses Cepstral analysis Conferences Neural networks Lung

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An integration of deep learning with feature fusion for protein-protein interaction prediction

An integration of deep learning with feature fusion for prot...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Hoai-Nhan Tran Phuc-Xuan-Quynh Nguyen Xiaoqing Peng Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

High-throughput biological and large-scale experiments for protein–protein interaction (PPI) identification provides valuable information about PPI networks but are time-consuming and limited in determining PPI between different species. We propose the integration of deep learning with feature fusion for PPI prediction, combining handcrafted features and protein sequence embedding for protein sequence representation. Our proposed method is evaluated on the Yeast full, Yeast core, Human, and eight independent datasets. The experimental results show that our method achieves 95.8% accuracy on the Yeast core, 99.2% accuracy on Human, and 100% accuracy on eight independent datasets. We also perform extensive comparisons with other existing outstanding methods and demonstrate the superior ability of the proposed method.

关键词： Deep learning Protein engineering Databases Neural networks Amino acids Protein sequence Biology

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A PTAS Framework for Clustering Problems in Doubling Metrics 29th

A PTAS Framework for Clustering Problems in Doubling Metri...

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29th International Computing and Combinatorics Conference, COCOON 2023

作者： Wu, Di Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

ISBN: (纸本)9783031491894

Constrained clustering problems have been studied extensively in recent years. In this paper, we focus on a class of constrained k-median problems with general constraints on facilities, denoted as GCF k-CMedian problems. We present a randomized polynomial-time approximation scheme (PTAS) framework based on the split-tree decomposition and dynamic programming process for GCF k-CMedian problems, such as k-median with service installation costs, k-facility location and priority k-median problems in doubling metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Dynamic programming

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LDAGSO: Predicting 1ncRNA-Disease Associations from Graph Sequences and Disease Ontology via Deep Learning techniques

LDAGSO: Predicting 1ncRNA-Disease Associations from Graph Se...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Norah Saeed Awn Yiming Li Baoying Zhao Min Zeng Min Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recent studies have confirmed the significant effects of long non-coding RNAs (1ncRNAs) in understanding the mechanism of diseases. Because of the relatively small number of validated associations between 1ncRNAs and diseases, and previous computational methods have limited performance without capturing important features of sequences and ontology information, we developed LDAGSO, a novel deep learning framework to predict 1ncRNA and disease associations from 1ncRNA sequences and disease ontology. For 1ncRNA sequences, we converted them into graph structure based on k-mer technique and de Bruijn graph, and captured high-level features of the graph using graph convolutional networks. For diseases, we extracted ontology term paths from the disease ontology tree, and treated them as sentences to obtain their feature representation using Bidirectional Encoder Representations from Transformers (BERT) technique. Finally, these two kinds of features were fed into a fully connected layer to perform the task of association prediction between 1ncRNAs and diseases. According to the results, our approach provides state-of-the-art results when evaluated by leave-one-out cross-validation.

关键词： Deep learning RNA Bit error rate Ontologies Feature extraction Transformers Task analysis

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