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Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 2531-2542页

作者： Ning, Qiao Zhao, Yaomiao Gao, Jun Chen, Chen Yin, Minghao Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214126 Peoples R China Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Jiangnan Univ Sch Fiber Engn & Equipment Technol Wuxi 214122 Peoples R China Northeast Normal Univ Informat Sci & Technol Changchun 130024 Peoples R China

MicroRNAs (miRNAs) play a significant role in cell differentiation, biological development as well as the occurrence and growth of diseases. Although many computational methods contribute to predicting the association between miRNAs and diseases, they do not fully explore the attribute information contained in associated edges between miRNAs and diseases. In this study, we propose a new method, Hierarchical Hypergraph learning in Association-Weighted heterogeneous network for MiRNA-Disease association identification (HHAWMD). HHAWMD first adaptively fuses multi-view similarities based on channel attention and distinguishes the relevance of different associated relationships according to changes in expression levels of disease-related miRNAs, miRNA similarity information, and disease similarity information. Then, HHAWMD assigns edge weights and attribute features according to the association level to construct an association-weighted heterogeneous graph. Next, HHAWMD extracts the subgraph of the miRNA-disease node pair from the heterogeneous graph and builds the hyperedge (a kind of virtual edge) between the node pair to generate the hypergraph. Finally, HHAWMD proposes a hierarchical hypergraph learning approach, including node-aware attention and hyperedge-aware attention, which aggregates the abundant semantic information contained in deep and shallow neighborhoods to the hyperedge in the hypergraph. Our experiment results suggest that HHAWMD has better performance and can be used as a powerful tool for miRNA-disease association identification.

关键词： Diseases Feature extraction Semantics Vectors Heterogeneous networks Data mining Correlation Biomedical measurement Aggregates Velocity measurement miRNA-disease association hierarchical hypergraph learning association-weighted heterogeneous network

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Guest Editorial for Selected Papers From BIOKDD 2021

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3068-3069页

作者： Yan, Da Qin, Zhaohui S. Bhattacharya, Debswapna Chen, Jake Y. Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Univ Alabama Birmingham Inst Informat Birmingham AL 35294 USA

The papers in this special section were presented at the 20th International Workshop on Data Mining in bioinformatics (BIOKDD 2021) that was held virtually on August 15, 2021. The conference featured the special theme of "Artificial intelligence in Medicine" which particularly welcomed paper submissions and invited talks related to the use of machine learning and data mining techniques for the analysis of large amounts of heterogeneous complex biological and medical data, with a particular focus on deep learning methods that see fast advancement and wider adoption in bioinformatics.

关键词： Special issues and sections Meetings bioinformatics Data mining

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CNNGRN: A Convolutional Neural Network-Based Method for Gene Regulatory Network Inference From Bulk Time-Series Expression Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 2853-2861页

作者： Gao, Zhen Tang, Jin Xia, Junfeng Zheng, Chun-Hou Wei, Pi-Jing Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230093 Anhui Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230093 Anhui Peoples R China Anhui Univ Coll Artificial Intelligence Hefei 230093 Anhui Peoples R China

Gene regulatory networks (GRNs) participate in many biological processes, and reconstructing them plays an important role in systems biology. Although many advanced methods have been proposed for GRN reconstruction, their predictive performance is far from the ideal standard, so it is urgent to design a more effective method to reconstruct GRN. Moreover, most methods only consider the gene expression data, ignoring the network structure information contained in GRN. In this study, we propose a supervised model named CNNGRN, which infers GRN from bulk time-series expression data via convolutional neural network (CNN) model, with a more informative feature. Bulk time series gene expression data imply the intricate regulatory associations between genes, and the network structure feature of ground-truth GRN contains rich neighbor information. Hence, CNNGRN integrates the above two features as model inputs. In addition, CNN is adopted to extract intricate features of genes and infer the potential associations between regulators and target genes. Moreover, feature importance visualization experiments are implemented to seek the key features. Experimental results show that CNNGRN achieved competitive performance on benchmark datasets compared to the state-of-the-art computational methods. Finally, hub genes identified based on CNNGRN have been confirmed to be involved in biological processes through literature.

关键词： Bulk gene expression deep learning gene regulatory network network visualization

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Explainable Drug Repurposing Approach From Biased Random Walks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 1009-1019页

作者： Castiglione, Filippo Nardini, Christine Onofri, Elia Pedicini, Marco Tieri, Paolo Inst Appl Comp Natl Res Council CNR IAC Italy I-00185 Rome RM Italy Roma Tre Univ Dept Math & Phys RM I-00146 Rome Italy

Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.

关键词： Drugs Diseases Databases Standards Data mining Sparse matrices Simultaneous localization and mapping Drug repurposing explainable artificial intelligence network medicine Markov chain biased random walk

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Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第6期20卷 3820-3829页

作者： Li, Yuguang Lu, Shuai Ma, Qiang Nan, Xiaofei Zhang, Shoutao Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Natl Supercomp Ctr Zhengzhou Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Sch Life Sci Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Sch Life Sci Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Longhu Lab Adv Immunol Zhengzhou 450001 Henan Peoples R China

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.

关键词： PPIs prediction sequenced-based CNNs attention mechanism

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Big Data Analytics in bioinformatics: Transforming Genomics and Proteomics for Advanced Biological Insights

Big Data Analytics in Bioinformatics: Transforming Genomics ...

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2024 International conference on Big Data Analytics in bioinformatics, DABCon 2024

作者： Vohra, D.S. Roorkee Geomatics Division RoorkeePC-247667 India

ISBN: (纸本)9798350378047

The integration of big data analytics into bioinformatics has revolutionized biological research and healthcare. This paper explores the methodologies, tools, and big data analytics use cases in bioinformatics, focusing on genomics, proteomics, and systems biology. Key methodologies include data collection and storage, preprocessing, and advanced data driven approaches like machine learning, statistical analysis, and visual analytics. Applications in genomics involve association studies across the genome and identification of gene diseases, while proteomics benefits from mass spectrometry data analysis. Systems biology leverages integrated data for comprehensive models of biological net/orks. Challenges such as data integration, computational complexity, and ethical concerns are discussed, alongside future directions involving advanced algorithms, improved data integration, and secure data-sharing technologies. This interdisciplinary domain holds immense potential for advancing science and medicine. © 2024 ieee.

关键词： Genome

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One-shot Biomedical Named Entity Recognition via Knowledge-Inspired Large Language Model 24

One-shot Biomedical Named Entity Recognition via Knowledge-I...

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15th conference on bioinformatics computational biology and Health Informatics

作者： Bian, Jnuyi Zheng, Jiaxuan Zhang, Yuyi Zhou, Hong Zhu, Shanfeng Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai Peoples R China Fudan Univ Software Sch Shanghai Peoples R China Atypon Syst LLC Oxford England Fudan Univ Zhangjiang Fudan Int Innovat Ctr Key Lab Computat Neurosci & Brain Inspired Intell Minist Educ Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798400713026

Large Language Models (LLMs) have demonstrated exceptional performance in numerous natural language processing tasks, particularly in generative tasks. Nevertheless, their performance in non-generative tasks, such as information extraction, especially within specialized domain-specific extraction tasks like Biomedical Named Entity Recognition (NER), has been less successful when applied in an unsupervised manner. To address this challenge, we draw inspiration from the chain-of-thought concept and adopt a two-step approach for NER using LLMs: entity span extraction and entity type determination. Additionally, we introduce a framework for incorporating domain-specific entity knowledge to mitigate the LLM's inherent lack of domain expertise during entity category determination. Experimental results from four biomedical NER datasets illustrate a significant improvement in our approach when compared to prior LLM-based methods. Furthermore, our approach achieves results on par with other few-shot methods using just one shot, in contrast to their requirement of 50 shots.

关键词： Biomedical Named Entity Recognition Large Language Model One-shot learning

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Tensor decomposition to identify context-aware spatial neighborhoods in the tumor microenvironment 46

Tensor decomposition to identify context-aware spatial neigh...

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46th Annual International conference of the ieee Engineering in Medicine and biology Society, EMBC 2024

作者： Kulkarni, Reva Patel, Gauri Eliason, Joel Rao, Arvind University of Michigan Department of Computational Medicine and Bioinformatics Ann ArborMI United States University of Michigan Department of Biomedical Engineering Ann ArborMI United States University of Michigan Department of Computational Medicine and Bioinformatics Department of Radiation Oncology Ann ArborMI United States

ISBN: (纸本)9798350371499

The tumor microenvironment (TME) is a complex ecosystem composed of diverse cell types whose spatial interactions play an important role in tumor progression. Many cell types have characteristic biomarkers that can be detected at single cell resolution through multiplex tissue imaging methods. There exist several image analysis tools to quantify such interactions, but they calculate summary statistics one image at a time. In this study, we use a scalable canonical polyadic (CP) tensor decomposition method to probe the spatial heterogeneity of cell type distributions simultaneously across samples. We use a publicly available colorectal cancer (CRC) dataset capturing 56 cell-surface markers from 35 patients across 136 images. Through analysis of tensor decomposition factors, we are able to identify tensor components corresponding to key cellular neighborhoods, such as bulk tumor and tumor edge. Wilcoxon rank sum tests on nine component decomposition identified components that varied significantly across Crohn's-like reaction (CLR) and diffuse inflammatory infiltration (DII) patient groups, including granulocyte enrichment in DII. Survival analysis identified distinct components associated with high tumor level and poor patient prognosis. © 2024 ieee.

关键词： Tumors

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An Automated Framework for Histopathological Nucleus Segmentation With Deep Attention Integrated Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第4期21卷 995-1006页

作者： Sun, Muyi Zou, Wenxuan Wang, Zhaoyang Wang, Song Sun, Zhenan Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Morden Post Automat Beijing Peoples R China Beijing Technol & Business Univ Sch Artificial Intelligence Beijing 100048 Peoples R China

Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanwhile, the accuracy is limited by the experience of the pathologist. Therefore, deep learning-empowered computer-aided diagnosis (CAD) is emerging as an important topic in digital pathology to streamline the standard process of automatic tissue analysis. Automated accurate nucleus segmentation can not only help pathologists make more accurate diagnosis, save time and labor, but also achieve consistent and efficient diagnosis results. However, nucleus segmentation is susceptible to staining variation, uneven nucleus intensity, background noises, and nucleus tissue differences in biopsy specimens. To solve these problems, we propose Deep Attention Integrated Networks (DAINets), which mainly built on self-attention based spatial attention module and channel attention module. In addition, we also introduce a feature fusion branch to fuse high-level representations with low-level features for multi-scale perception, and employ the mark-based watershed algorithm to refine the predicted segmentation maps. Furthermore, in the testing phase, we design Individual Color Normalization (ICN) to settle the dyeing variation problem in specimens. Quantitative evaluations on the multi-organ nucleus dataset indicate the priority of our automated nucleus segmentation framework.

关键词： Image segmentation Computer architecture Microprocessors Deep learning Pathology Biomedical imaging Training Attention mechanism deep learning histopathological image analysis nucleus segmentation

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Big Data Analytics on Lung Cancer Diagnosis Framework With Deep Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第4期21卷 757-768页

作者： Guan, Peiyuan Yu, Keping Wei, Wei Tan, Yanlin Wu, Jia Jiangxi Univ Chinese Med Sch Comp Sci & Technol Nanchang 330004 Jiangxi Peoples R China Waseda Univ Global Informat & Telecommun Inst Tokyo 1698555 Japan Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Cent South Univ Xiangya Hosp 2 PET CT Ctr Changsha 410083 Peoples R China Monash Univ Res Ctr Artificial Intelligence Clayton Vic 3800 Australia

As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristics of PET images, the framework uses a differential activation filter to select whole-body images containing lesion tissue. Second, a new neural network containing residual connections which has powerful generalization performance compared with normal FCN network is proposed for PET image reconstruction and denoising. Finally, in the segmentation of lesion tissues, a custom clustering algorithm based on the density is used to distinguishe the lesion tissue part from the normal tissue. Tests on real medical PET images show that the whole automated framework has good performance and time cost in PET lesion image screening, image denoising and lesion tissue segmentation compared with other algorithms. The framework shows promising scientific study and application prospects.

关键词： Image segmentation Lesions Positron emission tomography Noise reduction Neural networks Clustering algorithms Gaussian noise Deep learning image classification image noise reduction image segmentation cluster segmentation

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