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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Awn, Norah Saeed Li, Yiming Zhao, Baoying Zeng, Min Li, Min Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China

ISBN: (纸本)9781665468190

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. © 2022 IEEE.

关键词： Forecasting

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KEXNet:A Knowledge-Enhanced Model for Improved Chest X-Ray Lesion Detection

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Big Data Mining and Analytics 2024年第4期7卷 1187-1198页

作者： Quan Yan Junwen Duan Jianxin Wang School of Computer Science and Engineering and Hunan Provincial Key Lab on Bioinformatics Central South UniversityChangsha 410083China

Automated diagnosis of chest X-rays is pivotal in radiology,aiming to alleviate the workload of *** methods primarily rely on visual features or label dependence,which is a limitation in detecting nuanced or rare *** address this,we present KEXNet,a pioneering knowledge-enhanced X-ray lesion detection *** employs a unique strategy akin to expert radiologists,integrating a knowledge graph based on expert annotations with an interpretable graph learning *** novel method combines object detection with a graph neural network,facilitating precise local lesion *** global lesion detection,KEXNet synergizes knowledge-enhanced local features with global image features,enhancing diagnostic *** evaluations on three benchmark datasets demonstrate that KEXNet outshines existing models,particularly in identifying small or infrequent ***,on the Chest ImaGenome dataset,KEXNet’s AUC for local lesion detection surpasses 8.9%compared to the state-of-the-art method AnaXNet,showcasing its potential in revolutionizing automated chest X-ray diagnostics.

关键词： multi-label chest X-ray classification object detection knowledge graph learning

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Global Contrastive Learning with High-Quality Data in Large Vision-Language Models for Pathological Question Answering

Global Contrastive Learning with High-Quality Data in Large ...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Kuang, Hulin Liu, Suoni Zhang, Haojie Qu, Zhe Kan, Shichao Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350386226

Pathological question answering (PQA) is vital in computational pathology, as it involves interpreting pathological images and answering questions posed by humans. This interaction offers an effective means of engaging with users and enhancing the understanding of pathology-related information. Recent methods developed using large vision-language models (LVLMs), such as QUILT-LLAVA, have made significant progress in advancing PQA. However, existing models, such as those using the QUILT-1M dataset, neglect the quality of the training set during the fine-tuning stage, leading to sub-optimal performance. We recognize that high-quality training data can significantly enhance model performance. Therefore, we design a model-based data filtering strategy to remove images with obvious impurities from the instruction fine-tuning dataset. The filtered high-quality images are then used to fine-tune the model. Additionally, most existing vision-language alignment strategies focus primarily on aligning local features through next-word prediction, leading to a relatively homogeneous granularity in inter-modal alignment. To address this issue, we propose a global-wise alignment module, which introduces global-level contrastive learning during the pretraining stage to establish multi-granularity alignment between pathological images and language descriptions. Based on the above two processes, we design our method, named Global Contrastive Learning with High-Quality Data (GCL-HQD) for pathological question answering in LVLMs. Extensive experiments on two types of experimental settings demonstrate the effectiveness of the GCL-HQD method. © 2024 IEEE.

关键词： Contrastive Learning

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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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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Zhu, Lingzhi Zheng, Kai Duan, Guihua Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China Hunan Institute of Technology Hengyang School of Computer Science and Engineering Hunan421008 China

ISBN: (纸本)9781665468190

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. © 2022 IEEE.

关键词： Viruses

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Organization-Driven Business Process Configurable Modeling for Spatial Variability

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China Communications 2021年第11期18卷 229-242页

作者： Guosheng Kang Liqing Yang Liang Zhang Jianxun Liu Yiping Wen Hunan Provincial Key Lab.for Services Computing and Novel Software Technology Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science Fudan UniversityShanghai 201203China

Nowadays,enterprises need to continually adjust their business processes to adapt to the changes of business environments,especially when one business needs to be deployed in different application scenarios,which is called spatial variability in this *** the field of BPM(Business Process Management),configurable business process models have demonstrated their effectiveness in aspects of process modeling and model ***,we found that the existing techniques lead to complex configurable models,and are inadequate for model reuse especially for the spatial variability issue because they neglect the root impact of organizations on control flow.S-BPM(Subject-oriented Business Process Management)models provide a solid foundation for dealing with complex applications and help to bridge the gap between business and IT for process *** this paper,we propose an organization-driven business process configurable modeling approach for spatial variability by integrating both restriction and extension operations based on the S-BPM paradigm,in which business objects are also *** approach is validated with a general business process developed for the Real Estate Administration(REA)in a certain province of *** resulting configurable modeling framework can express the heterogeneous activity sequences for one business and has the potential to generate process models for uncertain environments in a new organization structure.

关键词： S-BPM configurable modeling organization-driven spatial variability business objects services

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Personalized federated adaptive regularization for heterogeneous medical image classification: COVID-19 CT results

Personalized federated adaptive regularization for heterogen...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yuan Qu, Zhe Wang, Shu Sheng, Tao Yu, Xiaoqing Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China Bank of Sanxiang Hunan Changsha China

ISBN: (纸本)9798350337488

During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an urgent need for the development of automated tools. Federated learning offers a privacypreserving solution to the challenge that limited annotated data within single healthcare facilities. However, existing federated aggregation strategies cannot adapt to the real-world medical image data problem of mixed heterogeneous. This paper introduces Personalized Federated Adaptive Regularization (pFedAR), an adaptive framework for federated learning that effectively utilizes multi-site COVID-19 CT datasets with mixed heterogeneous of distribution discrepancies. To solve the mixed distribution of label skew and feature shift, we propose a two-stage adaptive regularization. In the client training stage, we use the balance loss term to balance the COVID-19 clients with missing label. In the aggregation stage, we correct the client gradient conflict. Our method is developed and evaluated using eight real-world COVID-19 diagnosis datasets composed of CT images. Extensive experiments demonstrate the consistent improvement achieved by our method across the datasets. © 2023 IEEE.

关键词： federated learning heterogeneity medical image personalization

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MHLAT: Multi-Hop label-Wise Attention Model for Automatic ICD Coding 48

MHLAT: Multi-Hop Label-Wise Attention Model for Automatic IC...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike the single-pass reading process in previous works, humans tend to read the text and label definitions again to get more confident answers. Moreover, although pretrained language models have been used to address these problems, they suffer from huge memory usage. To address the above problems, we propose a simple but effective model called the Multi-Hop label-wise ATtention (MHLAT), in which multi-hop label-wise attention is deployed to get more precise and informative representations. Extensive experiments on three benchmark MIMIC datasets indicate that our method achieves significantly better or competitive performance on all seven metrics, with much fewer parameters to optimize. © 2023 IEEE.

关键词： bias-terms fine-tuning ICD coding multi-hop label-wise attention pre-trained language model

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Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

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Chinese Medical sciences Journal 2023年第1期38卷 38-48页

作者： Yu-Xia Guan Ying An Feng-Yi Guo Wei-Bai Pan Jian-Xin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric *** recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis *** we systematically summarize and analyze the ECGbased automatic analysis methods and ***,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing *** we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these ***,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

关键词： Electrocardiogram database preprocessing machine learning medical big data analysis

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GraphIsoFun: A graph neural network based approach for splice isoform function prediction

GraphIsoFun: A graph neural network based approach for splic...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Shuo Yang, Changhuo Li, Hong-Dong Wang, Jianxin Central South University School of Computer Science and Engineering Hunan Changsha410083 China Hunan Provincial Key Lab on Bioinformatics Central South University Hunan Changsha410083 China

ISBN: (纸本)9781665401265

Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators. © 2021 IEEE.

关键词： Graph neural networks

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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 ensem...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

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

ISBN: (纸本)9781665468190

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. © 2022 IEEE.

关键词： Genes

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