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31st International Conference on Computational Linguistics, COLING 2025

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

ISBN: (纸本)9798891761964

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. © 2025 Association for Computational Linguistics.

关键词： Data mining

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medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs 31

medIKAL: Integrating Knowledge Graphs as Assistants of LLMs ...

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31st International Conference on Computational Linguistics, COLING 2025

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

ISBN: (纸本)9798891761964

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. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of Computer Science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records

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Big Data Mining and Analytics 2025年第4期8卷 933-950页

作者： Abdulrahman Al-Dailami Hulin Kuang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Faculty of Computer and Information Technology Sana'a University Sana'a Yemen

Predicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges. The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data, enabling the effective and explainable fusion of the incomplete features. Modality-specific encoders are employed to encode each modality feature separately. To tackle the variability and incompleteness of input features among patients, a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs. Furthermore, a MultiModal Gated Contrastive Representation Learning (MMGCRL) method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance. We evaluate the proposed framework using the large-scale ICU dataset, MIMIC-III. Experimental results demonstrate its effectiveness in mortality prediction, outperforming several state-of-the-art methods.

关键词： Representation learning Accuracy MIMICs Mortality Logic gates Big Data Graph neural networks Data mining Electronic medical records

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FIND: Fine-grained Information Density Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis

arXiv

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arXiv 2025年

Retrieval-Augmented Large Language Models (LLMs), which integrate external knowledge into LLMs, have shown remarkable performance in various medical domains, including clinical diagnosis. However, existing RAG methods struggle to effectively assess task difficulty to make retrieval decisions, thereby failing to meet the clinical requirements for balancing efficiency and accuracy. So in this paper, we propose FIND (Fine-grained Information Density Guided Adaptive RAG), a novel framework that improves the reliability of RAG in disease diagnosis scenarios. FIND incorporates a fine-grained adaptive control module to determine whether retrieval is necessary based on the information density of the input. By optimizing the retrieval process and implementing a knowledge filtering module, FIND ensures that the retrieval is better suited to clinical scenarios. Experiments on three Chinese electronic medical record datasets demonstrate that FIND significantly outperforms various baseline methods, highlighting its effectiveness in clinical diagnosis tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Disease control

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks bioinformatics

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Improved kernels for triangle packing in tournaments

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Science China(Information Sciences) 2023年第5期66卷 100-113页

作者： Hanchun YUAN Qilong FENG Jianxin WANG School of Computer Science and Engineering Central South University Hunan Provincial Key Lab on Bioinformatics Central South University

Triangle packing problem has been paid lots of attention to in the literature. In this paper, we study the kernelization of the triangle packing problem in tournaments. For the parameterized arc-disjoint triangle packing problem in tournaments, we find a maximal arc-disjoint triangle packing with the number of vertices bounded by 2.5k. Based on the relation between the maximal arc-disjoint triangle packing and the vertices outside of the packing, a kernel of size 3.5k for the problem is obtained, improving the previous best one 6k. For the parameterized vertex-disjoint triangle packing problem in sparse tournaments, several new properties between the triangles in maximal vertex-disjoint triangle packing and the arcs in the feedback arc set are presented, which result in a kernel of size 7k for the problem, improving the previous best one of *** also give a 7k vertex kernel for the parameterized feedback vertex set problem in sparse *** kernelization process presented in this paper for the parameterized arc-disjoint triangle packing can be applied to solve the parameterized arc-disjoint triangle packing problem on other restricted directed graph classes.

关键词： tournaments triangle packing kernelization fixed-parameter tractable graph algorithms

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DeepRetention:A Deep Learning Approach for Intron Retention Detection

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Big Data Mining and Analytics 2023年第2期6卷 115-126页

作者： Zhenpeng Wu Jiantao Zheng Jiashu Liu Cuixiang Lin Hong-Dong Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

As the least understood mode of alternative splicing,Intron Retention(IR)is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease *** methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained *** metrics are not able to describe the pattern of sequencing depth of intronic reads,which is an intuitive and informative characteristic of retained *** hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR *** we present DeepRetention,a novel approach for IR detection by modeling the pattern of sequencing depth of *** to the lack of a gold standard dataset of IR,we first compare DeepRetention with two state-of-the-art methods,*** and IRFinder,on simulated RNA-seq datasets with retained *** results show that DeepRetention outperforms these two ***,DeepRetention performs well when it is applied to third-generation long-read RNA-seq data,while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing ***,we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer’s Disease(AD)*** differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene *** parent genes of differential IRs are enriched in AD-related *** summary,DeepRetention detects IR from a new angle of view,providing a valuable tool for IR analysis.

关键词： Alternative Splicing(AS) Intron Retention(IR) intronic reads distribution pattern RNA-seq

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A Comparison of Computational Approaches for Intron Retention Detection

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Big Data Mining and Analytics 2022年第1期5卷 15-31页

作者： Jiantao Zheng Cuixiang Lin Zhenpeng Wu Hong-Dong Li the Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

Intron Retention(IR)is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most *** has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex *** efforts have been dedicated to the development of IR detection *** methods differ in their metrics to quantify retention propensity,performance to detect IR events,functional enrichment of detected IRs,and computational speed.A systematic experimental comparison would be valuable to the selection and use of existing *** this work,we conduct an experimental comparison of existing IR detection *** the unavailability of a gold standard dataset of intron retention,we compare the IR detection performance on simulation ***,we compare the IR detection results with real RNA-Seq *** also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment,which is illustrated on an Alzheimer’s disease RNA-Seq *** discuss key principles and features of existing approaches and outline their *** systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.

关键词： alternative splicing intron retention gene expression RNA-Seq

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A Data-Driven Clustering Recommendation Method for Single-Cell RNA-Sequencing Data

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Tsinghua Science and Technology 2021年第5期26卷 772-789页

作者： Yu Tian Ruiqing Zheng Zhenlan Liang Suning Li Fang-Xiang Wu Min Li the Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityHunan 410083China the Division of Biomedical Engineering and Department of Mechanical Engineering University of SaskatchewanSKS7N5A9Canada

Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell *** of the critical steps in cellular heterogeneity analysis is the cell type *** scRNA-seq clustering methods have been proposed to partition cells into *** all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality *** this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological *** analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in *** by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or ***,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq *** perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

关键词： single-cel RNA-sequencing(sc RNA-seq) cel ular heterogeneity cel type identification data latent shape clustering

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