Current Bioinformatics最新文献_第2页

LBSA-DRIVER: A Novel Approach to Identifying Cancer Driver Genes Using List-Based Simulated Annealing LBSA-DRIVER：利用基于列表的模拟退火法识别癌症驱动基因的新方法

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-07-15 DOI: 10.2174/0115748936302984240604061302

Yilmaz Atay, Lionel Alangeh Ngobesing, Mustafa Ozgur Cingiz

{"title":"LBSA-DRIVER: A Novel Approach to Identifying Cancer Driver Genes Using List-Based Simulated Annealing","authors":"Yilmaz Atay, Lionel Alangeh Ngobesing, Mustafa Ozgur Cingiz","doi":"10.2174/0115748936302984240604061302","DOIUrl":"https://doi.org/10.2174/0115748936302984240604061302","url":null,"abstract":"Introduction: Cancer driver genes are genes responsible for cancer genesis; thus, identifying cancer-related genes is crucial in fostering cancer treatment. The accuracy in identifying cancer driver genes within the vast pool of normal passenger genes directly influences the efficacy of treatment approaches. Objective: This research aimed to effectively identify cancer driver genes using the List-based Simulated Annealing (LBSA) optimization technique. Method: The proposed model (LBSA-DRIVER) harnesses a list-based simulated annealing algorithm within a bipartite network to pinpoint cancer driver genes. The process begins with creating a bipartite graph that integrates gene mutations and expression data from carefully chosen datasets. The LBSA algorithm is then applied to the generated graph to identify and rank the genes, drawing insights from a biological interaction network. Result: Following the algorithm's development, rigorous experimental analyses have been conducted using four benchmark datasets from The Cancer Genome Atlas (TCGA) database. The datasets used were the Breast Cancer dataset (BRCA), Prostate Adenocarcinoma dataset (PRAD), Ovarian cancer dataset (OV), and Glioblastoma Multiforme dataset (GBM). Conclusion: Our findings, including precision, recall, F-score, and accuracy metrics, provide strong evidence of the effectiveness of the proposed model in identifying driver genes.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720127","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

MFTP-Tool: A Wide & Deep Learning Framework for Multi-Functional Therapeutic Peptides Prediction MFTP 工具：用于多功能治疗肽预测的广泛深度学习框架

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-07-10 DOI: 10.2174/0115748936299646240625092734

Yang Lv, Ting Liu, YuChen Ma, Hongqiang Lyu, Ze Liu

{"title":"MFTP-Tool: A Wide & Deep Learning Framework for Multi-Functional Therapeutic Peptides Prediction","authors":"Yang Lv, Ting Liu, YuChen Ma, Hongqiang Lyu, Ze Liu","doi":"10.2174/0115748936299646240625092734","DOIUrl":"https://doi.org/10.2174/0115748936299646240625092734","url":null,"abstract":"Background: The identification and functional prediction of Multifunctional Therapeutic Peptides (MFTP) play a pivotal role in drug discovery, particularly for conditions such as inflammation and hyperglycemia. Current computational methods exhibit limitations in their ability to accurately predict the multifunctionality of these peptides. Methods: We propose a novel Wide and Deep Learning Framework that integrates both deep learning and machine learning approaches. The deep learning segment processes word vectors using a neural network model, while the wide segment utilizes the physicochemical properties of peptides in a random forest-based model. This hybrid approach aims to enhance the accuracy of MFTP function prediction. Results: Our framework outperformed the existing PrMFTP predictor in terms of precision, coverage, accuracy, and absolute true values. The evaluation was conducted on both training and independent testing datasets, demonstrating the robustness and generalizability of our model. Conclusion: The proposed Wide & Deep Learning Framework offers a significant advancement in the computational prediction of MFTP functions. The availability of our model through a userfriendly web interface at MFTP-Tool.m6aminer.cn provides a valuable tool for researchers in the field of therapeutic peptide-based drug discovery, potentially accelerating the development of new treatments.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586134","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

Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning 基于卷积神经网络和集合学习的胎儿超声图像标准平面自动检测技术

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-07-10 DOI: 10.2174/0115748936295679240620094626

Baoping Zhu, Fan Yang, Hongliang Duan, Zhipeng Gao

{"title":"Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning","authors":"Baoping Zhu, Fan Yang, Hongliang Duan, Zhipeng Gao","doi":"10.2174/0115748936295679240620094626","DOIUrl":"https://doi.org/10.2174/0115748936295679240620094626","url":null,"abstract":"aims: This study aims to leverage artificial intelligence for enhancing medical diagnosis, focusing on ultrasound evaluation of fetal development and detection of fetal diseases. background: Traditional diagnostic methods in ultrasound are known for being time-consuming and laborious, prompting the need for more efficient approaches. objective: The objective of this research is to develop an end-to-end automatic diagnosis system using convolutional neural networks with ensemble learning to enhance robustness and accuracy in classifying ultrasound images. method: The study involves constructing and implementing the automatic diagnosis system, training it on a diverse dataset encompassing six categories: abdomen, brain, femur, thorax, maternal cervix, and other planes. result: Experimental results demonstrate that the proposed end-to-end system significantly improves the detection accuracy of the standard plane in ultrasound images. conclusion: The application of artificial intelligence through an ensemble learning-based automatic diagnosis system shows promise in advancing ultrasound-based medical diagnosis, particularly in fetal development assessment. other: This research contributes to the ongoing efforts in leveraging technology for more efficient and accurate medical diagnostic processes.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586136","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

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models 子宫内膜异位症的等级矩阵法：整合数据并构建诊断模型

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-07-10 DOI: 10.2174/0115748936296151240605053713

Ranze Xie, Deqing Hong, Jiaqi Yuan, Peng Xu, Wenbin Liu, Zheng Ye

{"title":"Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models","authors":"Ranze Xie, Deqing Hong, Jiaqi Yuan, Peng Xu, Wenbin Liu, Zheng Ye","doi":"10.2174/0115748936296151240605053713","DOIUrl":"https://doi.org/10.2174/0115748936296151240605053713","url":null,"abstract":"Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586217","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

GenRepAI: Utilizing Artificial Intelligence to Identify Repeats in Genomic Suffix Trees GenRepAI：利用人工智能识别基因组后缀树中的重复序列

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-07-10 DOI: 10.2174/0115748936303435240702112205

Freeson Kaniwa

{"title":"GenRepAI: Utilizing Artificial Intelligence to Identify Repeats in Genomic Suffix Trees","authors":"Freeson Kaniwa","doi":"10.2174/0115748936303435240702112205","DOIUrl":"https://doi.org/10.2174/0115748936303435240702112205","url":null,"abstract":"Background: The human genome is densely populated with repetitive DNA sequences that play crucial roles in genomic functions and structures but are also implicated in over 40 human diseases. The computational challenge of identifying and characterizing these repeats is significant due to the complexity and size of the genome, which are overwhelming traditional algorithms. Methods: To address these challenges, we propose GenRepAI, a deep learning framework to navigate and analyze genomic suffix trees. GenRepAI employs supervised machine learning classifiers trained on labeled datasets of repeat annotations and unsupervised anomaly detection to identify novel repeat sequences. The models are trained using convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and vision transformers to classify and annotate repeats within the human genome. Results: GenRepAI is designed to comprehensively profile repeats that underlie various neurological diseases, allowing researchers to identify pathogenic expansions. The framework will integrate into existing genomic analysis pipelines, with the capability to screen patient genomes and highlight potential causal variants for further validation. Conclusion: GenRepAI is set to become a foundational tool in genomics, leveraging artificial intelligence to enhance the characterization of repetitive sequences. It promises significant advancements in the molecular diagnosis of repeat expansion disorders and contributes to a deeper understanding of genomic structure and function, with broad applications in personalized medicine.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586135","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

Screening Analysis of Predictive Markers for Cytokine Release Syndrome Risk in CAR-T Cell Therapy CAR-T 细胞疗法中细胞因子释放综合征风险预测标记物的筛选分析

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-07-03 DOI: 10.2174/0115748936295986240619162816

Jiayu Xu, Chengkui Zhao, Zhenyu Wei, Weixin Xie, Qi Cheng, Min Zhang, Shuangze Han, Liqing Kang, Nan Xu, Lei Yu, Weixing Feng

{"title":"Screening Analysis of Predictive Markers for Cytokine Release Syndrome Risk in CAR-T Cell Therapy","authors":"Jiayu Xu, Chengkui Zhao, Zhenyu Wei, Weixin Xie, Qi Cheng, Min Zhang, Shuangze Han, Liqing Kang, Nan Xu, Lei Yu, Weixing Feng","doi":"10.2174/0115748936295986240619162816","DOIUrl":"https://doi.org/10.2174/0115748936295986240619162816","url":null,"abstract":"Background: Chimeric Antigen Receptor (CAR)-T cell therapy has emerged as a highly effective treatment for hematological tumors. However, the associated adverse reaction, Cytokine Release Syndrome (CRS), poses a significant challenge. While numerous studies have investigated CRS biomarkers during CAR-T cell therapy, the ability to predict CRS risk prior to treatment initiation remains a crucial yet underexplored aspect. Objective: The primary purpose of this study was to address the issue of limited data, explore an alternative approach using public data to identify predictive markers for CRS risk assessment from RNA-Seq in pre-treatment patients data, and comprehend the inducible mechanisms underlying CRS. Methods: We integrated information from two public databases, the FDA Adverse Event Reporting System (FAERS) for adverse reaction reports of CAR-T cell therapy and the Cancer Genome Atlas (TCGA) for RNA-Seq data on corresponding hematological tumors. Candidate genes were screened by correlation analysis between Reported Odds Ratio (ROR) values and RNA-Seq gene expression levels, and then core factors were identified through stepwise analysis of pathway enrichment, cluster analysis, and protein interactions. Results: Our analysis highlighted the correlation between CRS risk and pre-treatment T cell activation/ proliferation, identifying key genes (IFN-γ, IL1β, IL2, IL6, and IL10) as significant CRS indicators. Conclusion: This study offers a unique perspective on predicting CRS risk before CAR-T cell therapy, circumventing the challenges of scarce clinical data by leveraging analysis of public databases. It elucidates the crucial role of T cell activation/proliferation dynamics in CRS. The analytical methods and identified markers provide a reference for the research and clinical application of CAR-T cell therapy.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550364","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

Insights into Co-Expression Network Analysis of MicroProteins and their Target Transcription Factors in Plant Embryo Development 植物胚胎发育过程中微蛋白及其目标转录因子共表达网络分析的启示

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-06-26 DOI: 10.2174/0115748936304167240530091051

Khadijeh Shokri, Naser Farrokhi, Asadollah Ahmadikhah, Mahdi Safaeizade, Amir Mousavi

{"title":"Insights into Co-Expression Network Analysis of MicroProteins and their Target Transcription Factors in Plant Embryo Development","authors":"Khadijeh Shokri, Naser Farrokhi, Asadollah Ahmadikhah, Mahdi Safaeizade, Amir Mousavi","doi":"10.2174/0115748936304167240530091051","DOIUrl":"https://doi.org/10.2174/0115748936304167240530091051","url":null,"abstract":"Background: Gene expression is regulated in a spatiotemporal manner, and the roles of microProteins (MiPs) in this concept have started to become clear in plants. Methods: Here, a microarray data analysis was carried out to decipher the spatiotemporal role of MiPs in embryo development. The guilt-by-association method was used to determine the corresponding regulatory factors. Results: Module network analyses and protein-protein interaction (PPI) assays suggested 13 modules for embryo development in the Arabidopsis model plant. Various biological processes such as metabolite biosynthesis, hormone transition and regulation, fatty acid and storage protein biosynthesis, and photosynthesis-related processes were prevalent. Different transcription factors (TFs) at different stages of embryo development were found and reviewed. Furthermore, 106 putative MiPs were identified that might be involved in the regulation of embryo development. Candidate hub MiPs (15) at embryo developmental stages were identified by PPI network analysis and their putative regulatory roles were discussed. Previously reported MiPs, AT1G14760 (KNOX), AT5G39860 (PRE1), and AT2G46410 (CPC), were noted to be present in modules M3 and M8. Conclusion: Molecular comprehension of regulatory factors including MiPs and TFs during embryo development allows targeted breeding of the corresponding traits and genome-based engineering of value-added new varieties.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529313","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

Detection of DNA N6-Methyladenine Modification through SMRT-seq Features and Machine Learning Model 通过 SMRT-seq 特征和机器学习模型检测 DNA N6-甲基腺嘌呤修饰

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-06-26 DOI: 10.2174/0115748936300671240523044154

Yichu Guo, Yixuan Zhang, Xiaoqing Liu, Pingan He, Yuni Zeng, Qi Dai

引用次数: 0

Multinomial Logistic Regression with Adaptive Regularization for Cancer Subtype Classification via Multi-omics Data 利用自适应正则化的多项式逻辑回归，通过多组学数据进行癌症亚型分类

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-06-24 DOI: 10.2174/0115748936308171240605075531

Yingdi Wu, Fuzhen Cao, Juntao Li

{"title":"Multinomial Logistic Regression with Adaptive Regularization for Cancer Subtype Classification via Multi-omics Data","authors":"Yingdi Wu, Fuzhen Cao, Juntao Li","doi":"10.2174/0115748936308171240605075531","DOIUrl":"https://doi.org/10.2174/0115748936308171240605075531","url":null,"abstract":"Background: Integrating multi-omics data for cancer classification brings complementary biological insights while also facing challenges such as data integration, gene grouping, and adaptive weight construction. Objective: This paper aims to address the challenges faced by the cancer subtype classification and gene screening based on multi-omics data. Methods: Multinomial logistic regression with adaptive regularization (MLRAR) was proposed by integrating DNA methylation, gene mutation, and RNA-seq information. A data preprocessing strategy that effectively utilizes multi-omics information was presented, and the local maximum quasiclique merging (lmQCM) algorithm was implemented to group genes. Biological pathway information was utilized to evaluate the significance of gene groups, while the significance of each gene within a group was evaluated by integrating mutation information, information theory, and methylation information. Results: Compared to MRlasso, MRGL, MSGL, MROGL, AMRSOGL, and AGLRMR, the proposed method yielded improvements in subtype classification accuracy of breast cancer by 2.6%, 2.9%, 3.5%, 2.3%, 2.0%, and 1.8%, respectively. In addition, MLRAR also achieved significant improvements in ovarian cancer by 8.2%, 5.0%, 6.8%, 5.2%, 12.7%, and 6.3%, respectively. Conclusion: The proposed method can effectively deal with data integration, gene grouping, and adaptive weight construction.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506060","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

EPI-HAN: Identification of Enhancer Promoter Interaction Using Hierarchical Attention Network EPI-HAN：利用层次注意网络识别增强子启动子相互作用

IF 4 3区生物学

Current Bioinformatics Pub Date : 2024-06-12 DOI: 10.2174/0115748936294743240524113731

Fatma S. Ahmed, Saleh Aly, X. Liu

{"title":"EPI-HAN: Identification of Enhancer Promoter Interaction Using Hierarchical Attention Network","authors":"Fatma S. Ahmed, Saleh Aly, X. Liu","doi":"10.2174/0115748936294743240524113731","DOIUrl":"https://doi.org/10.2174/0115748936294743240524113731","url":null,"abstract":"\u0000\u0000Enhancer-Promoter Interaction (EPI) recognition is crucial for understanding\u0000human development and transcriptional regulation. EPI in the genome plays a significant role in\u0000regulating gene expression. In Genome-Wide Association Studies (GWAS), EPIs help to improve\u0000the mechanistic understanding of disease- or trait-associated genetic variants.\u0000\u0000\u0000\u0000Experimental methods for classifying EPIs are time-consuming and expensive. Consequently,\u0000there has been a growing emphasis on research focused on developing computational approaches\u0000that leverage deep learning and other machine learning techniques. One of the main challenges\u0000in EPI prediction is the long sequences of enhancers and promoters, which most existing computational\u0000approaches struggle with. This paper proposes a new deep learning model based on the Hierarchical\u0000Attention Network (HAN) for EPI detection. The proposed EPI-HAN model has two\u0000unique features: (i) a hybrid embedding strategy (ii) a hierarchical HAN structure comprising two\u0000attention layers that operate at both the individual token and smaller sequence levels.\u0000\u0000\u0000\u0000In benchmark comparisons, the EPI-HAN model demonstrates superior performance over\u0000state-of-the-art methods, as evidenced by AUROC and AUPR metrics for specific cell lines. Specifically,\u0000for the cell lines HeLa-S3, HUVEC, and NHEK, the AUROC values are 0.962, 0.946, and\u00000.987, respectively, and the AUPR values are 0.842, 0.724, and 0.926, respectively.\u0000\u0000\u0000\u0000The comparative results indicate that our model surpasses other state-of-the-art models\u0000in three out of six cell lines. The Superior performance in recognizing EPIs is attributed to the hierarchical\u0000structure of the attention mechanism.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350099","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