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

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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Modeling Gene Regulatory Network Based on Non-homogeneous Dynamic Bayesian Network 11

Modeling Gene Regulatory Network Based on Non-homogeneous Dy...

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11th International Conference on bioinformatics and computational biology (ICBCB)

作者： Hu Chunling Zhang Qianqian Zhang Jiayao Hefei Uni Coll Artificial Intelligence & Big Data Hefei Peoples R China

ISBN: (纸本)9798350397871

Among methods for reconstructing gene regulatory networks, non-homogeneous dynamic Bayesian networks have received much attention because of their advantages of expressing both the regulatory relationships among genes and the strengths of relationships among genes. However, such models sample the parent node set randomly, and the selection of the candidate parent node set does not take into account the correlation between nodes, resulting in a low convergence speed of network reconstruction. This paper has proposed a non-homogeneous dynamic Bayesian network model based on parent node filtering (PF-NH-DBN). Firstly, PF-NH-DBN uses mutual information and time-series conditional mutual information to screen the initial set of candidate parent nodes. Thereby reducing the search space and further removing redundant edges, making network reconstruction more accurate. Secondly, the mutual information based on the Gaussian mutation strategy is proposed to avoid falling into the local optimum. Finally, the experimental results on synthetic and on real biological network data show that the new model yields better network reconstruction accuracies than the original model.

关键词： gene regulatory network non-homogeneous dynamic Bayesian network mutual information network structure inference

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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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Predicting miRNA-Disease Associations via Meta-Path Embedding

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 911-922页

作者： Wang, Lei Huang, Xun Liu, Xueming Huazhong Univ Sci & Technol MOE Engn Res Ctr Autonomous Intelligent Unmanned S Sch Artificial Intelligence & Automat State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

MicroRNAs (miRNAs) play a critical role in various biological processes, making the accurate identification of miRNA-disease associations essential for understanding human diseases. Existing methods for predicting these associations often lack interpretability. In this study, we introduce MNEMDA, a novel computational method that leverages meta-path-based network embedding (metapath2vec) to extract features from a heterogeneous network of miRNA-disease interactions. Using the XGBoost classifier, MNEMDA predicts potential miRNA-disease associations. Our approach achieves an overall AUC of 0.959 and an overall AUPR of 0.952, outperforming several state-of-the-art models with an average AUC score of 0.958 across 15 common diseases. Case studies on renal cell carcinoma and breast neoplasms confirm MNEMDA's efficacy, with 93% and 97% of the top 30 predicted miRNAs, respectively, validated by recent experimental reports. Survival analysis also verifies the effectiveness and practical value of MNEMDA. Additionally, MNEMDA maintains excellent performance on other datasets, demonstrating its generalization ability. Moreover, MNEMDA shows insensitivity to input data noise, providing a robust and interpretable tool for predicting miRNA-disease associations.

关键词： Diseases Vectors Semantics Predictive models Heterogeneous networks Biological system modeling Feature extraction Databases Data models Data mining miRNA-disease association network embedding meta-path heterogeneous network

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Exploratory Analysis of Topic Interests and Their Evolution in bioinformatics Research Using Semantic Text Mining and Probabilistic Topic Modeling

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ieee ACCESS 2022年 10卷 31480-31493页

作者： Gurcan, Fatih Cagiltay, Nergiz Ercil Karadeniz Tech Univ Fac Engn Dept Comp Engn TR-61080 Trabzon Turkey Atilim Univ Fac Engn Dept Software Engn TR-06830 Ankara Turkey

bioinformatics, which has developed rapidly in recent years with the collaborative contributions of the fields of biology and informatics, provides a deeper perspective on the analysis and understanding of complex biological data. In this regard, bioinformatics has an interdisciplinary background and a rich literature in terms of domain-specific studies. Providing a holistic picture of bioinformatics research by analyzing the major topics and their trends and developmental stages is critical for an understanding of the field. From this perspective, this study aimed to analyze the last 50 years of bioinformatics studies (a total of 71,490 articles) by using an automated text-mining methodology based on probabilistic topic modeling to reveal the main topics, trends, and the evolution of the field. As a result, 24 major topics that reflect the focuses and trends of the field were identified. Based on the discovered topics and their temporal tendencies from 1970 until 2020, the developmental periods of the field were divided into seven phases, from the "newborn" to the "wisdom" stages. Moreover, the findings indicated a recent increase in the popularity of the topics "Statistical Estimation", "Data Analysis Tools", "Genomic Data", "Gene Expression", and "Prediction". The results of the study revealed that, in bioinformatics studies, interest in innovative computing and data analysis methods based on artificial intelligence and machine learning has gradually increased, thereby marking a significant improvement in contemporary analysis tools and techniques based on prediction.

关键词： bioinformatics Market research biology Analytical models Genomics Proteins computational modeling bioinformatics corpus probabilistic topic modeling textual content analysis scientometric analysis bioinformatics topics and trends

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Harnessing Digital Pathology And Causal Learning To Improve Eosinophilic Esophagitis Dietary Treatment Assignment 20

Harnessing Digital Pathology And Causal Learning To Improve ...

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20th ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Aknin, Eliel Larey, Ariel Caldwell, Julie M. Collins, Margaret H. Rothenberg, Marc E. Savir, Yonatan Technion Fac Med & Ind Engn Dept Physiol Biophys & Syst Biol Haifa Israel Technion Fac Med & Comp Sci Dept Physiol Biophys & Syst Biol Haifa Israel Univ Cincinnati Coll Med Dept Pediat Cincinnati Childrens Hosp Med CtrDiv Allergy & I Cincinnati OH USA Univ Cincinnati Coll Med Div Pathol & Lab Med Cincinnati Childrens Hosp Med CtrDept Pediat Cincinnati OH USA Technion Dept Physiol Biophys & Syst Biol Fac Med Haifa Israel

ISBN: (纸本)9798350310177

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. Diagnosis of EoE heavily relies on counting eosinophils in histological slides, a manual, laborious, time-consuming task that limits the ability to extract complex patient-dependent features. The treatment for EoE typically involves a combination of medication and dietary changes, particularly food elimination. A personalized, tailor-made food elimination plan is crucial for the patient's engagement and treatment efficiency. Previous attempts to predict the best food-elimination strategy did not yield significant results. In this work, on the one hand, we utilize AI for inferring many histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the "Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study" (SOFEED), where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This work demonstrates the importance of AI in examining the distribution of molecular features within hist

关键词： Machine Learning Causal Learning Eosinophilic Esophagitis Personalized Medicine Treatment Assignment Clinical Decision Support Artificial intelligence

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SIG: Graph-Based Cancer Subtype Stratification With Gene Mutation Structural Information

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 1752-1764页

作者： Zhang, Chengcheng Li, Wei Deng, Ming Jiang, Yizhang Cui, Xiaohui Chen, Ping Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214126 Jiangsu Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi 214126 Jiangsu Peoples R China Jiangnan Univ Engn Res Ctr Intelligent Technol Healthcare Minist Educ Wuxi 214126 Jiangsu Peoples R China Wuhan Univ Zhongnan Hosp Dept Radiol Wuhan 430071 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430071 Peoples R China Univ Massachusetts Boston Dept Engn Boston MA 02125 USA

Somatic tumors have a high-dimensional, sparse, and small sample size nature, making cancer subtype stratification based on somatic genomic data a challenge. Current methods for improving cancer clustering performance focus on dimension reduction, integrating multi-omics data, or generating realistic samples, yet ignore the associations between mutated genes within the patient-gene matrix. We refer to these associations as gene mutation structural information, which implicitly includes cancer subtype information and can enhance subtype clustering. We introduce a novel method for cancer subtype clustering called SIG(Structural Information within Graph). As cancer is driven by a combination of genes, we establish associations between mutated genes within the same patient sample, pair by pair, and use a graph to represent them. An association between two mutated genes corresponds to an edge in the graph. We then merge these associations among all mutated genes to obtain a structural information graph, which enriches the gene network and improves its relevance to cancer clustering. We integrate the somatic tumor genome with the enriched gene network and propagate it to cluster patients with mutations in similar network regions. Our method achieves superior clustering performance compared to SOTA methods, as demonstrated by clustering experiments on ovarian and LUAD datasets.

关键词： Cancer Tumors Genomics bioinformatics Clustering algorithms Germanium Task analysis Cancer clustering feature selection gene mutat ion structural information genomic data graph

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Artificial intelligence-Driven Image Analysis of Bacterial Cells and Biofilms

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第1期20卷 174-184页

作者： Ragi, Shankarachary Rahman, Md Hafizur Duckworth, Jamison Jawaharraj, Kalimuthu Chundi, Parvathi Gadhamshetty, Venkataramana South Dakota Mines Rapid City SD 57701 USA Univ Nebraska Omaha NE 68182 USA

The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate reducing bacteria that are implicated in microbiologically influenced corrosion problems. Our goal is to automate the process of extracting the geometrical properties of the DA-G20 cells from the scanning electron microscopy (SEM) images, which is otherwise a laborious and costly process. These geometric properties are a biofilm phenotype that allow us to understand how the biofilm structurally adapts to the surface properties of the underlying metals, which can lead to better corrosion prevention solutions. We adapt two deep learning models: ( a) a deep convolutional neural network ( DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model to achieve instance segmentation of the cells. These models are then integrated with moment invariants approach to measure the geometric characteristics of the segmented cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are 227x and 70x faster respectively, compared to the traditional method of manual identification and measurement of the cell geometric properties by the domain experts.

关键词： Sulfate-reducing bacteria biofilms deep learning for microscopy biofilm image segmentation

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PSPGO: Cross-Species Heterogeneous Network Propagation for Protein Function Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第3期20卷 1713-1724页

作者： Wu, Kaitao Wang, Lexiang Liu, Bo Liu, Yang Wang, Yadong Li, Junyi Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Ctr Bioinformat Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen Guangdong Peoples R China

How to use computational methods to effectively predict the function of proteins remains a challenge. Most prediction methods based on single species or single data source have some limitations: the former need to train different models for different species, the latter only to infer protein function from a single perspective, such as the method only using Protein-Protein Interaction (PPI) network just considers the protein environment but ignore the intrinsic characteristics of protein sequences. We found that in some network-based multi-species methods the networks of each species are isolated, which means there is no communication between networks of different species. To solve these problems, we propose a cross-species heterogeneous network propagation method based on graph attention mechanism, PSPGO, which can propagate feature and label information on sequence similarity (SS) network and PPI network for predicting gene ontology terms. Our model is evaluated on a large multi-species dataset split based on time and is compared with several state-of-the-art methods. The results show that our method has good performance. We also explore the predictive performance of PSPGO for a single species. The results illustrate that PSPGO also performs well in prediction for single species.

关键词： Protein function annotation protein network graph neural network gene ontology

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