IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献_第9页

Enhancing Generalizability in Biomedical Entity Recognition: Self-Attention PCA-CLS Model 增强生物医学实体识别的通用性：自我关注 PCA-CLS 模型。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-16 DOI: 10.1109/TCBB.2024.3429234

Rajesh Kumar Mundotiya;Juhi Priya;Divya Kuwarbi;Teekam Singh

{"title":"Enhancing Generalizability in Biomedical Entity Recognition: Self-Attention PCA-CLS Model","authors":"Rajesh Kumar Mundotiya;Juhi Priya;Divya Kuwarbi;Teekam Singh","doi":"10.1109/TCBB.2024.3429234","DOIUrl":"10.1109/TCBB.2024.3429234","url":null,"abstract":"One of the primary tasks in the early stages of data mining involves the identification of entities from biomedical corpora. Traditional approaches relying on robust feature engineering face challenges when learning from available (un-)annotated data using data-driven models like deep learning-based architectures. Despite leveraging large corpora and advanced deep learning models, domain generalization remains an issue. Attention mechanisms are effective in capturing longer sentence dependencies and extracting semantic and syntactic information from limited annotated datasets. To address out-of-vocabulary challenges in biomedical text, the PCA-CLS (Position and Contextual Attention with CNN-LSTM-Softmax) model combines global self-attention and character-level convolutional neural network techniques. The model's performance is evaluated on eight distinct biomedical domain datasets encompassing entities such as genes, drugs, diseases, and species. The PCA-CLS model outperforms several state-of-the-art models, achieving notable F\u0000<inline-formula><tex-math>$_{1}$</tex-math></inline-formula>\u0000-scores, including 88.19% on BC2GM, 85.44% on JNLPBA, 90.80% on BC5CDR-chemical, 87.07% on BC5CDR-disease, 89.18% on BC4CHEMD, 88.81% on NCBI, and 91.59% on the s800 dataset.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1934-1941"},"PeriodicalIF":3.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations 利用机器学习技术检测蛋白质功能：调查、实验和经验评估。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-15 DOI: 10.1109/TCBB.2024.3427381

Kamal Taha

{"title":"Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations","authors":"Kamal Taha","doi":"10.1109/TCBB.2024.3427381","DOIUrl":"10.1109/TCBB.2024.3427381","url":null,"abstract":"This review article delves deeply into the various machine learning (ML) methods and algorithms employed in discerning protein functions. Each method discussed is assessed for its efficacy, limitations, potential improvements, and future prospects. We present an innovative hierarchical classification system that arranges algorithms into intricate categories and unique techniques. This taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. The study incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank: (1) individual techniques under the same methodology sub-category, (2) different sub-categories within the same category, and (3) the broad categories themselves. Integrating the innovative methodological classification, empirical findings, and experimental assessments, the article offers a well-rounded understanding of ML strategies in protein function identification. The paper also explores techniques for multi-task and multi-label detection of protein functions, in addition to focusing on single-task methods. Moreover, the paper sheds light on the future avenues of ML in protein function determination.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1965-1986"},"PeriodicalIF":3.6,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141619844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOTHER-DB: A Database for Sharing Nonhuman Ovarian Histology Images MOTHER-DB：共享非人类卵巢组织学图像的数据库。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-12 DOI: 10.1109/TCBB.2024.3426999

Suzanne W. Dietrich;Wenli Ma;Yian Ding;Karen H. Watanabe;Mary B. Zelinski;James P. Sluka

{"title":"MOTHER-DB: A Database for Sharing Nonhuman Ovarian Histology Images","authors":"Suzanne W. Dietrich;Wenli Ma;Yian Ding;Karen H. Watanabe;Mary B. Zelinski;James P. Sluka","doi":"10.1109/TCBB.2024.3426999","DOIUrl":"10.1109/TCBB.2024.3426999","url":null,"abstract":"The goal of the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) project is to establish a collection of nonhuman ovary histology images for multiple species as a resource for researchers and educators. An important component of sharing scientific data is the inclusion of the contextual metadata that describes the data. MOTHER extends the Ecological Metadata Language (EML) for documenting research data, leveraging its data provenance and usage license with the inclusion of metadata for ovary histology images. The design of the MOTHER metadata includes information on the donor animal, including reproductive cycle status, the slide and its preparation. MOTHER also extends the ezEML tool, called ezEML+MOTHER, for the specification of the metadata. The design of the MOTHER database (MOTHER-DB) captures the metadata about the histology images, providing a searchable resource for discovering relevant images. MOTHER also defines a curation process for the ingestion of a collection of images and its metadata, verifying the validity of the metadata before its inclusion in the MOTHER collection. A Web search provides the ability to identify relevant images based on various characteristics in the metadata itself, such as genus and species, using filters.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2598-2603"},"PeriodicalIF":3.6,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141599244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-SeqCNN: A Novel Light-Weight Bi-Directional CNN Architecture for Protein Function Prediction Bi-SeqCNN：用于蛋白质功能预测的新型轻量级双向 CNN 架构

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-11 DOI: 10.1109/TCBB.2024.3426491

Vikash Kumar;Akshay Deepak;Ashish Ranjan;Aravind Prakash

{"title":"Bi-SeqCNN: A Novel Light-Weight Bi-Directional CNN Architecture for Protein Function Prediction","authors":"Vikash Kumar;Akshay Deepak;Ashish Ranjan;Aravind Prakash","doi":"10.1109/TCBB.2024.3426491","DOIUrl":"10.1109/TCBB.2024.3426491","url":null,"abstract":"Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both \u0000short-and-long\u0000 range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on \u0000short-term\u0000 information from both the past and the future, although they offer parallelism. Therefore, a novel \u0000bi-directional CNN\u0000 that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN\u0000<inline-formula><tex-math>$^+$</tex-math></inline-formula>\u0000 is an ensemble approach to better the prediction results. To our knowledge, this is the first time \u0000bi-directional CNNs\u0000 are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50–0.70 times) fewer parameters than the SOTA methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1922-1933"},"PeriodicalIF":3.6,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141590210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SCRN: Single-Cell Gene Regulatory Network Identification in Alzheimer's Disease SCRN：阿尔茨海默病的单细胞基因调控网络鉴定。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-08 DOI: 10.1109/TCBB.2024.3424400

Wentao Zhu;Zhiqiang Du;Ziang Xu;Defu Yang;Minghan Chen;Qianqian Song

{"title":"SCRN: Single-Cell Gene Regulatory Network Identification in Alzheimer's Disease","authors":"Wentao Zhu;Zhiqiang Du;Ziang Xu;Defu Yang;Minghan Chen;Qianqian Song","doi":"10.1109/TCBB.2024.3424400","DOIUrl":"10.1109/TCBB.2024.3424400","url":null,"abstract":"Alzheimer's disease (AD) is the most common neurodegenerative disease, and it consumes considerable medical resources with increasing number of patients every year. Mounting evidence show that the regulatory disruptions altering the intrinsic activity of genes in brain cells contribute to AD pathogenesis. To gain insights into the underlying gene regulation in AD, we proposed a graph learning method, Single-Cell based Regulatory Network (SCRN), to identify the regulatory mechanisms based on single-cell data. SCRN implements the γ-decaying heuristic link prediction based on graph neural networks and can identify reliable gene regulatory networks using locally closed subgraphs. In this work, we first performed UMAP dimension reduction analysis on single-cell RNA sequencing (scRNA-seq) data of AD and normal samples. Then we used SCRN to construct the gene regulatory network based on three well-recognized AD genes (APOE, CX3CR1, and P2RY12). Enrichment analysis of the regulatory network revealed significant pathways including NGF signaling, ERBB2 signaling, and hemostasis. These findings demonstrate the feasibility of using SCRN to uncover potential biomarkers and therapeutic targets related to AD.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1886-1896"},"PeriodicalIF":3.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data 基于时间序列数据的基因调控网络推断的改进型模糊认知图。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-04 DOI: 10.1109/TCBB.2024.3423383

Marzieh Emadi;Farsad Zamani Boroujeni;Jamshid Pirgazi

{"title":"Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data","authors":"Marzieh Emadi;Farsad Zamani Boroujeni;Jamshid Pirgazi","doi":"10.1109/TCBB.2024.3423383","DOIUrl":"10.1109/TCBB.2024.3423383","url":null,"abstract":"Microarray data provide lots of information regarding gene expression levels. Due to the large amount of such data, their analysis requires sufficient computational methods for identifying and analyzing gene regulation networks; however, researchers in this field are faced with numerous challenges such as consideration for too many genes and at the same time, the limited number of samples and their noisy nature of the data. In this paper, a hybrid method base on fuzzy cognitive map and compressed sensing is used to identify interactions between genes. For this purpose, in inference of the gene regulation network, the Ensemble Kalman filtered compressed sensing is used to learn the fuzzy cognitive map. Using the Ensemble Kalman filter and compressed sensing, the fuzzy cognitive map will be robust against noise. The proposed algorithm is evaluated using several metrics and compared with several well know methods such as LASSOFCM, KFRegular, CMI2NI. The experimental results show that the proposed method outperforms methods proposed in recent years in terms of SSmean, Data Error and accuracy.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1816-1829"},"PeriodicalIF":3.6,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141534365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder 基于屏蔽图自动编码器的潜在 miRNA 与疾病关联预测

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-02 DOI: 10.1109/TCBB.2024.3421924

Hailin Feng;Chenchen Ke;Quan Zou;Zhechen Zhu;Tongcun Liu

{"title":"Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder","authors":"Hailin Feng;Chenchen Ke;Quan Zou;Zhechen Zhu;Tongcun Liu","doi":"10.1109/TCBB.2024.3421924","DOIUrl":"10.1109/TCBB.2024.3421924","url":null,"abstract":"Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1874-1885"},"PeriodicalIF":3.6,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification 基于自我监督学习的图卷积网络用于脑疾病分类

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-02 DOI: 10.1109/TCBB.2024.3422152

Guangyu Wang;Ying Chu;Qianqian Wang;Limei Zhang;Lishan Qiao;Mingxia Liu

{"title":"Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification","authors":"Guangyu Wang;Ying Chu;Qianqian Wang;Limei Zhang;Lishan Qiao;Mingxia Liu","doi":"10.1109/TCBB.2024.3422152","DOIUrl":"10.1109/TCBB.2024.3422152","url":null,"abstract":"Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, graph convolutional networks (GCNs) can be naturally used in the identification of BFN, and can generally achieve an encouraging performance if given large amounts of training data. In practice, however, it is very difficult to obtain sufficient brain functional data, especially from subjects with brain disorders. As a result, GCNs usually fail to learn a reliable feature representation from limited BFNs, leading to overfitting issues. In this paper, we propose an improved GCN method to classify brain diseases by introducing a self-supervised learning (SSL) module for assisting the graph feature representation. We conduct experiments to classify subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) respectively from normal controls (NCs). Experimental results on two benchmark databases demonstrate that our proposed scheme tends to obtain higher classification accuracy than the baseline methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1830-1841"},"PeriodicalIF":3.6,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Structured Matrix Approximation for Robustness to Incomplete Biosequence Data 针对不完整生物序列数据的最佳结构化矩阵近似。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-01 DOI: 10.1109/TCBB.2024.3420903

Chris Salahub;Jeffrey Uhlmann

引用次数: 0

Ense-i6mA: Identification of DNA N6-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning Ense-i6mA：利用 XGB-RFE 特征选择和集合机器学习识别 DNA N6-甲基腺嘌呤位点。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-01 DOI: 10.1109/TCBB.2024.3421228

Xueqiang Fan;Bing Lin;Jun Hu;Zhongyi Guo

{"title":"Ense-i6mA: Identification of DNA N6-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning","authors":"Xueqiang Fan;Bing Lin;Jun Hu;Zhongyi Guo","doi":"10.1109/TCBB.2024.3421228","DOIUrl":"10.1109/TCBB.2024.3421228","url":null,"abstract":"DNA N\u00006\u0000-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. However, experimental methods for detecting 6mA sites are high-priced and time-consuming. In this study, we propose a novel computational method, called Ense-i6mA, to predict 6mA sites. Firstly, five encoding schemes, i.e., one-hot encoding, gcContent, Z-Curve, \u0000<italic>K\u0000-mer nucleotide frequency, and \u0000<italic>K\u0000-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, eXtreme gradient boosting coupled with recursive feature elimination is applied to remove noisy features for avoiding over-fitting, reducing computing time and complexity. Then, the best subset of features is fed into base-classifiers composed of Extra Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Finally, to minimize generalization errors, the prediction probabilities of the base-classifiers are aggregated by averaging for inferring the final 6mA sites results. We conduct experiments on two species, i.e., Arabidopsis thaliana and Drosophila melanogaster, to compare the performance of Ense-i6mA against the recent 6mA sites prediction methods. The experimental results demonstrate that the proposed Ense-i6mA achieves area under the receiver operating characteristic curve values of 0.967 and 0.968, accuracies of 91.4% and 92.0%, and Mathew's correlation coefficient values of 0.829 and 0.842 on two benchmark datasets, respectively, and outperforms several existing state-of-the-art methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1842-1854"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0