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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

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.

关键词： Measurement Codes Speech coding MIMICs Benchmark testing Signal processing Acoustics

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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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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

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

ISBN: (纸本)9781665429825

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.

关键词： Proteins Protein engineering Costs Annotations Databases Splicing Conferences

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Direct-to-consumer genetic testing in China and its role in GWAS discovery and replication

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Quantitative Biology 2021年第2期9卷 201-215页

作者： Kang Kang Xue Sun Lizhong Wang Xiaotian Yao Senwei Tang Junjie Deng Xiaoli Wu WeGene Research Team Can Yang Gang Chen Shenzhen WeGene Clinical Laboratory Shenzhen 518118China WeGene Shenzhen Zaozhidao Technology Co.Ltd.Shenzhen 518042China Department of Mathematics The Hong Kong University of Science and TechnologyHong KongChina Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

Background:The direct-to-consumer genetic testing(DTC-GT)industry has exploded in recent years,initiated by market pioneers from the United States and quickly followed by companies from Europe and *** addition to their primary objective of providing ancestry and health information to customers,DTC-GT services have emerged as a valuable data resource for large-scale population and genetics ***:We assessed DTC-GT market leaders in the *** China,user participation in research,and academic reports based on this *** also investigated DTC-GT end-user value by tracing key updates of companies provided via health risk reports and evaluating their predictive *** then assessed the replicability of several genome-wide association studies(GWAS)based on a Chinese DTC-GT ***:As recent entrants to the market,Chinese DTC-GT serv ice providers have published less academic research than their Western counterparts;however,a larger proportion of Chinese users consent to participate in research *** increases in user volume and resultant report updates led to reclassification of some users'polygenic risk levels,but within a reasonable scale and with increased predictive *** among GWAS using the Chinese DTC-GT biobank varied by studied trait,population background,and sample ***:We speculate that the rapid growth in DTC-GT services,particularly in non-Caucasian populations,will yield an important and much-needed resource for biobanking,large-scale genetic studies,clinical trials,and post-clinical applications.

关键词： DTC-GT biobank Chinese population polygenic risk GWAS replication

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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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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

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

ISBN: (纸本)9781665401265

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. © 2021 IEEE.

关键词： RNA

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

arXiv

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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

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs

arXiv

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arXiv 2024年

作者： Jia, Mingyi Duan, Junwen Song, Yan Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University China University of Science and Technology of China China

Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (Integrating Knowledge Graphs as Assistants of LLMs), a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. medIKAL assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. It innovatively employs a residual network-like approach, allowing initial diagnosis by LLMs to be merged into KG search results. Through a path-based reranking algorithm and a fill-in-the-blank style prompt template, it further refined the diagnostic process. We validated medIKAL’s effectiveness through extensive experiments on a newly introduced open-sourced Chinese EMR dataset, demonstrating its potential to improve clinical diagnosis in real-world settings. The code and dataset are publicly available at https://***/CSU-NLP-Group/mediKAL. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Local Adaptive U-net for Medical Image Segmentation

Local Adaptive U-net for Medical Image Segmentation

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ning Liu Liangliang Liu Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Pingdingshan University Pingdingshan China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Medical image segmentation is the primary measure of medical image analysis. With the development of deep learning, U-net based approaches have presented for different medical image segmentation tasks. However, the pooling and the simple convolution operation for deep feature maps in the U-shaped network would lead to the coarse segmentation result. In this paper, we design a local adaptive U-net (LA U-net) for medical image segmentation. There are two major modules: the Local Adaptive Module (LAM) and Multi-scale Convolution Module (MCM) in the network. The LAM get more feature maps from each down-sampling process. The MCM capture more global information for the encoding path. To validate the proposed network's performance, we verify it on two datasets: DRIVE dataset, and ISIC 2018 dataset; the results show that LA U-net achieves superior performance on two datasets.

关键词： Image segmentation Biomedical imaging Convolution Feature extraction Task analysis Lesions Sensitivity

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Improving human essential protein prediction using only protein sequences via ensemble learning

Improving human essential protein prediction using only prot...

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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

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