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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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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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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Yao, Jingxiao Liu, Jin Cheng, Jianhong Kuang, Hulin Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang550009 China

ISBN: (纸本)9798350337488

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 (M3CI-Net), which pay more attention to the different characteristics information of different MRI modalities T1, T2, T1ce, Flair. In M3CI-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. © 2023 IEEE.

关键词： Edge detection Glioma segmentation IDH genotyping Multi-modality Pre-fusion

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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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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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RUIE: Retrieval-based Unified Information Extraction using Large Language Model

arXiv

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arXiv 2024年

作者： Liao, Xincheng Duan, Junwen Huang, Yixi Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Hunan Changsha China

Unified information extraction (UIE) aims to extract diverse structured information from unstructured text. While large language models (LLMs) have shown promise for UIE, they require significant computational resources and often struggle to generalize to unseen tasks. We propose RUIE (Retrieval-based Unified Information Extraction), a framework that leverages in-context learning for efficient task generalization. RUIE introduces a novel demonstration selection mechanism combining LLM preferences with a keyword-enhanced reward model, and employs a bi-encoder retriever trained through contrastive learning and knowledge distillation. As the first trainable retrieval framework for UIE, RUIE serves as a universal plugin for various LLMs. Experimental results on eight held-out datasets demonstrate RUIE’s effectiveness, with average F1-score improvements of 19.22 and 3.22 compared to instruction-tuning methods and other retrievers, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

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Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

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Chinese Journal of Electronics 2021年第5期30卷 793-814页

作者： PAN Yi LIU Jin TIAN Xu LAN Wei GUO Rui Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South University School of Computer Electronics and Information Guangxi University

The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the indepth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size,irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently,some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally,objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

关键词： biomedical MRI brain image segmentation machine learning methods brain hippocampal segmentation methods medical image processing neurophysiology related brain diseases diseases learning (artificial intelligence) hippocampus brain MRI images

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FedMMR: Multi-Modal Federated Learning via Missing Modality Reconstruction

FedMMR: Multi-Modal Federated Learning via Missing Modality ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shu Wang Zhe Qu Yuan Liu Shichao Kan Yixiong Liang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Federated Learning (FL) presents a decentralized learning approach for privacy preservation. While many research focuses on uni-modal FL, a more intricate version, multimodal FL, uncovers fundamental attributes. Clients might only gather specific modalities, incurring missing modalities in multi-modal FL. The missing modality problem incurs modality heterogeneity among clients and loses inter-modal connection within one client. To address these issues, we introduce a novel multi-modal FL method called Federated Missing Modality Reconstruction (FedMMR), which tackles these dual challenges through two distinct facets. First, we devise a cross-modal reconstruction policy for synthesizing absent modalities from other modalities. This aligns the data feature spaces across clients, thereby alleviating bias in resultant local models. Subsequently, we steer local models to maintain an inter-modal awareness of both existing and reconstructed modalities, recognizing their potential as complementary components. Comprehensive results show that FedMMR outperforms existing FL baselines.

关键词： Weight measurement Privacy Federated learning Predictive models Data models

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Local-Global Spatio-Temporal Encoding with Adaptive Brain Region Signal Segmentation for Mild Cognitive Impairment Detection

Local-Global Spatio-Temporal Encoding with Adaptive Brain Re...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Jie Wang Luohuang Wu Hulin Kuang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal variations in brain connectivity and fail to model temporal features effectively. To address these limitations, we propose a spatio-temporal graph Transformer framework. First, we segment rs-fMRI data into distinct segments using the multivariate Gaussian distribution of brain region signals and remove redundant segments based on cumulative Jensen-Shannon divergence. Next, we extract features for each brain region and time point within segments, using these temporal features to guide spatial feature aggregation. A position encoder is designed to leverage temporal features across segments to capture both local and global spatio-temporal information. Finally, we employ a population graph framework as the classifier, with spatio-temporal features and demographic data of each subject forming the edge weights, to output the detection results. Our method achieves 92.80%, 81.89%, and 82.80% accuracy in MCI detection, early MCI detection, and late MCI detection tasks on the ADNI3 and ADNI2 datasets, respectively, outperforming existing methods.

关键词： Image segmentation Accuracy Image edge detection Gaussian distribution Functional magnetic resonance imaging Feature extraction Transformers Brain modeling Encoding bioinformatics

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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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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： Training Pathology Filtering Impurities Pipelines Training data Contrastive learning Chatbots Question answering (information retrieval) Data models

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Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to K-Level Stochastic Optimizations 41

Stability and Generalization for Stochastic Recursive Moment...

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41st International Conference on Machine Learning, ICML 2024

作者： Pan, Xiaokang Li, Xingyu Liu, Jin Sun, Tao Sun, Kai Chen, Lixing Qu, Zhe Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Department of Computer Science Tulane University New Orleans United States College of Computer National University of Defense Technology Changsha China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Institute of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China

STOchastic Recursive Momentum (STORM)based algorithms have been widely developed to solve one to K-level (K ≥ 3) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient issue and achieve near-optimal convergence results. However, there is relatively little work on understanding their generalization performance, particularly evident during the transition from one to K-level optimization contexts. This paper provides a comprehensive generalization analysis of three representative STORM-based algorithms: STORM, COVER, and SVMR, for one, two, and K-level stochastic optimizations under both convex and strongly convex settings based on algorithmic stability. Firstly, we define stability for K-level optimizations and link it to generalization. Then, we detail the stability results for three prominent STORM-based algorithms. Finally, we derive their excess risk bounds by balancing stability results with optimization errors. Our theoretical results provide strong evidence to complete STORM-based algorithms: (1) Each estimator may decrease their stability due to variance with its estimation target. (2) Every additional level might escalate the generalization error, influenced by the stability and the variance between its cumulative stochastic gradient and the true gradient. (3) Increasing the batch size for the initial computation of estimators presents a favorable trade-off, enhancing the generalization performance. Copyright 2024 by the author(s)

关键词： Optimization algorithms

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