: Inflammatory Bowel Disease (IBD), encompassing ulcerative colitis and Crohn’s disease, affects millions worldwide. Characterized by a complex interplay of genetic, microbial, and environmental factors, IBD challenges conventional treatment approaches, necessitating precision medicine. This paper reviews the role of bioinformatics in leveraging transcriptional data for novel IBD diagnostics and therapeutics. It highlights the genomic landscape of IBD, focusing on genetic factors and insights from genome-wide association studies. The interrelation between the gut microbiome and host transcriptional responses in IBD is examined, emphasizing the use of bioinformatics tools in deciphering these interactions. Our study synthesizes developments in transcriptomics and proteomics, revealing aberrant gene and protein expression patterns linked to IBD pathogenesis. We advocate for the integration of multi-omics data, underscoring the complexity and necessity of bioinformatics in interpreting these datasets. This approach paves the way for personalized treatment strategies, improved disease prognosis, and enhanced patient care. The insights provided offer a comprehensive overview of IBD, highlighting bioinformatics as key in advancing personalized healthcare in IBD management.
{"title":"Mining Transcriptional Data for Precision Medicine: Bioinformatics Insights into Inflammatory Bowel Disease","authors":"Arman Shahriari, Shokoofeh Amirzadeh Shams, Hamidreza Mahboobi, Maryam Yazdanparast, Amirreza Jabbaripour Sarmadian","doi":"10.2174/0115748936302814240729062857","DOIUrl":"https://doi.org/10.2174/0115748936302814240729062857","url":null,"abstract":": Inflammatory Bowel Disease (IBD), encompassing ulcerative colitis and Crohn’s disease, affects millions worldwide. Characterized by a complex interplay of genetic, microbial, and environmental factors, IBD challenges conventional treatment approaches, necessitating precision medicine. This paper reviews the role of bioinformatics in leveraging transcriptional data for novel IBD diagnostics and therapeutics. It highlights the genomic landscape of IBD, focusing on genetic factors and insights from genome-wide association studies. The interrelation between the gut microbiome and host transcriptional responses in IBD is examined, emphasizing the use of bioinformatics tools in deciphering these interactions. Our study synthesizes developments in transcriptomics and proteomics, revealing aberrant gene and protein expression patterns linked to IBD pathogenesis. We advocate for the integration of multi-omics data, underscoring the complexity and necessity of bioinformatics in interpreting these datasets. This approach paves the way for personalized treatment strategies, improved disease prognosis, and enhanced patient care. The insights provided offer a comprehensive overview of IBD, highlighting bioinformatics as key in advancing personalized healthcare in IBD management.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178124","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}
Background: Proteins play a vital role in sustaining life, requiring the formation of specific 3D structures to manifest their essential biological functions. Structure comparison techniques are benefiting from the ever-expanding repositories of the Protein Data Bank. The development of computational tools for protein and amino acid 3D structural comparisons plays an important role in understanding protein functions. The Triangular Spatial Relationship (TSR)-based was developed for such purpose. Methods: A parallelization strategy and actual implementation on high-performance clusters using the distributed and shared memory programming model, along with the utilization of multi-core CPU and many-core GPU accelerators, were developed. 3D structures of proteins and amino acids are represented by an integer vector in the TSR-based method. This parallelization strategy is designed for the TSR-based method for large-scale 3D structural comparisons of proteins and amino acids in this study. It can also be adapted to other applications where a vector type of data structure is used. Results: Due to the nature of the vector representation of protein and amino acid structures using the TSR-based method, the comparison algorithm is well-suited for parallelization on large scale supercomputers. Performance studies on the representative datasets were conducted to demonstrate the efficiency of the parallelization strategy. It allows comparisons of large 3D protein or amino acid structure datasets to finish within a reasonable amount of time. Conclusion: The case studies, by taking advantage of this parallelization code, demonstrate that applying either mirror image or feature selection in the TSR-based algorithms improves the classifications of protein and amino acid 3D structures. The TSR keys have the advantage of performing structure-based BLAST searches. The parallelization code could be used as a reference for similar future studies.
背景：蛋白质在维持生命方面发挥着至关重要的作用，需要形成特定的三维结构才能体现其基本生物功能。结构比较技术得益于蛋白质数据库不断扩大的资源库。蛋白质和氨基酸三维结构比较计算工具的开发在了解蛋白质功能方面发挥着重要作用。基于三角空间关系（TSR）的计算工具就是为此而开发的。方法：利用分布式和共享内存编程模型，同时利用多核 CPU 和多核 GPU 加速器，开发了一种并行化策略，并在高性能集群上实际实施。在基于 TSR 的方法中，蛋白质和氨基酸的三维结构由整数向量表示。在本研究中，这种并行化策略是为基于 TSR 的方法设计的，用于蛋白质和氨基酸的大规模三维结构比较。它也可适用于使用矢量类型数据结构的其他应用。结果由于使用基于 TSR 的方法对蛋白质和氨基酸结构进行矢量表示的性质，该比较算法非常适合在大型超级计算机上进行并行化。对代表性数据集进行的性能研究证明了并行化策略的效率。它允许在合理的时间内完成大型三维蛋白质或氨基酸结构数据集的比较。结论利用该并行化代码进行的案例研究表明，在基于 TSR 的算法中应用镜像或特征选择可以改进蛋白质和氨基酸三维结构的分类。TSR 密钥具有执行基于结构的 BLAST 搜索的优势。该并行化代码可作为今后类似研究的参考。
{"title":"A Parallel Implementation for Large-Scale TSR-based 3D Structural Comparisons of Protein and Amino Acid","authors":"Feng Chen, Tarikul I. Milon, Poorya Khajouie, Antoinette Myers, Wu Xu","doi":"10.2174/0115748936306625240724102438","DOIUrl":"https://doi.org/10.2174/0115748936306625240724102438","url":null,"abstract":"Background: Proteins play a vital role in sustaining life, requiring the formation of specific 3D structures to manifest their essential biological functions. Structure comparison techniques are benefiting from the ever-expanding repositories of the Protein Data Bank. The development of computational tools for protein and amino acid 3D structural comparisons plays an important role in understanding protein functions. The Triangular Spatial Relationship (TSR)-based was developed for such purpose. Methods: A parallelization strategy and actual implementation on high-performance clusters using the distributed and shared memory programming model, along with the utilization of multi-core CPU and many-core GPU accelerators, were developed. 3D structures of proteins and amino acids are represented by an integer vector in the TSR-based method. This parallelization strategy is designed for the TSR-based method for large-scale 3D structural comparisons of proteins and amino acids in this study. It can also be adapted to other applications where a vector type of data structure is used. Results: Due to the nature of the vector representation of protein and amino acid structures using the TSR-based method, the comparison algorithm is well-suited for parallelization on large scale supercomputers. Performance studies on the representative datasets were conducted to demonstrate the efficiency of the parallelization strategy. It allows comparisons of large 3D protein or amino acid structure datasets to finish within a reasonable amount of time. Conclusion: The case studies, by taking advantage of this parallelization code, demonstrate that applying either mirror image or feature selection in the TSR-based algorithms improves the classifications of protein and amino acid 3D structures. The TSR keys have the advantage of performing structure-based BLAST searches. The parallelization code could be used as a reference for similar future studies.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881446","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}
Pub Date : 2024-08-02DOI: 10.2174/0115748936300759240712061707
Xia Chen, Qiang Qu, Xiang Zhang, Hao Nie, Xiuxiu Chao, Weihao Ou, Haowen Chen, Xiangzheng Fu
Aim: MicroRNAs (miRNAs), pivotal regulators in various biological processes, are closely linked to human diseases. This study aims to propose a computational model, SIDMF, for predicting miRNA-disease associations. Background: Computational methods have proven efficient in predicting miRNA-disease associations, leveraging functional similarity and network-based inference. Machine learning techniques, including support vector machines, semi-supervised algorithms, and deep learning models, have gained prominence in this domain. Objective: Develop a computational model that integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework to predict potential associations between miRNAs and diseases accurately. Methods: SIDMF, introduced in this study, integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework. Through the reconstruction of the miRNA-disease association matrix, SIDMF predicts potential associations between miRNAs and diseases. Results: The performance of SIDMF was evaluated using global Leave-One-Out Cross-Validation (LOOCV) and local LOOCV, achieving high Area Under the Curve (AUC) values of 0.9536 and 0.9404, respectively. Comparative analysis against other methods demonstrated the superior performance of SIDMF. Case studies on breast cancer, esophageal cancer, and prostate cancer further validated SIDMF's predictive accuracy, with a substantial percentage of the top 50 predicted miRNAs confirmed in relevant databases. Conclusion: SIDMF emerges as a promising computational model for predicting potential associations between miRNAs and diseases. Its robust performance in global and local evaluations, along with successful case studies, underscores its potential contributions to disease prevention, diagnosis, and treatment.
{"title":"Prediction of miRNA-disease Associations by Deep Matrix Decomposition Method based on Fused Similarity Information","authors":"Xia Chen, Qiang Qu, Xiang Zhang, Hao Nie, Xiuxiu Chao, Weihao Ou, Haowen Chen, Xiangzheng Fu","doi":"10.2174/0115748936300759240712061707","DOIUrl":"https://doi.org/10.2174/0115748936300759240712061707","url":null,"abstract":"Aim: MicroRNAs (miRNAs), pivotal regulators in various biological processes, are closely linked to human diseases. This study aims to propose a computational model, SIDMF, for predicting miRNA-disease associations. Background: Computational methods have proven efficient in predicting miRNA-disease associations, leveraging functional similarity and network-based inference. Machine learning techniques, including support vector machines, semi-supervised algorithms, and deep learning models, have gained prominence in this domain. Objective: Develop a computational model that integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework to predict potential associations between miRNAs and diseases accurately. Methods: SIDMF, introduced in this study, integrates disease semantic similarity and miRNA functional similarity within a deep matrix factorization framework. Through the reconstruction of the miRNA-disease association matrix, SIDMF predicts potential associations between miRNAs and diseases. Results: The performance of SIDMF was evaluated using global Leave-One-Out Cross-Validation (LOOCV) and local LOOCV, achieving high Area Under the Curve (AUC) values of 0.9536 and 0.9404, respectively. Comparative analysis against other methods demonstrated the superior performance of SIDMF. Case studies on breast cancer, esophageal cancer, and prostate cancer further validated SIDMF's predictive accuracy, with a substantial percentage of the top 50 predicted miRNAs confirmed in relevant databases. Conclusion: SIDMF emerges as a promising computational model for predicting potential associations between miRNAs and diseases. Its robust performance in global and local evaluations, along with successful case studies, underscores its potential contributions to disease prevention, diagnosis, and treatment.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881443","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}
Pub Date : 2024-08-02DOI: 10.2174/0115748936306893240720192301
QingLan Ma, Jingxin Ren, Lei Chen, Wei Guo, KaiYan Feng, Tao Huang, Yu-Dong Cai
Background: Accurately predicting survival in hospitalized COVID-19 patients is crucial but challenging due to multiple risk factors. This study addresses the limitations of existing research by proposing a comprehensive machine-learning framework to identify key mortality risk factors and develop a robust predictive model. Objective: This study proposes an analytical framework that leverages various machine learning techniques to predict the survival of hospitalized COVID-19 patients accurately. The framework comprehensively evaluates multiple clinical indicators and their associations with mortality risk. Method: Patient data, including gender, age, health condition, and smoking habits, was divided into discharged (n=507) and deceased (n=300) categories. Each patient was characterized by 92 clinical features. The framework incorporated seven feature ranking algorithms (LASSO, LightGBM, MCFS, mRMR, RF, CATBoost, and XGBoost), the IFS method, and four classification algorithms (DT, KNN, RF, and SVM). Results: Age, diabetes, dyspnea, chronic kidney failure, and high blood pressure were identified as the most important risk factors. The best model achieved an F1-score of 0.857 using KNN with 34 selected features. Conclusion: Our findings provide a comprehensive analysis of COVID-19 mortality risk factors and develops a robust predictive model. The findings highlight the increased risk in patients with comorbidities, consistent with existing literature. The proposed framework can aid in developing personalized treatment plans and allocating healthcare resources effectively.
{"title":"Identifying Key Clinical Indicators Associated with the Risk of Death in Hospitalized COVID-19 Patients","authors":"QingLan Ma, Jingxin Ren, Lei Chen, Wei Guo, KaiYan Feng, Tao Huang, Yu-Dong Cai","doi":"10.2174/0115748936306893240720192301","DOIUrl":"https://doi.org/10.2174/0115748936306893240720192301","url":null,"abstract":"Background: Accurately predicting survival in hospitalized COVID-19 patients is crucial but challenging due to multiple risk factors. This study addresses the limitations of existing research by proposing a comprehensive machine-learning framework to identify key mortality risk factors and develop a robust predictive model. Objective: This study proposes an analytical framework that leverages various machine learning techniques to predict the survival of hospitalized COVID-19 patients accurately. The framework comprehensively evaluates multiple clinical indicators and their associations with mortality risk. Method: Patient data, including gender, age, health condition, and smoking habits, was divided into discharged (n=507) and deceased (n=300) categories. Each patient was characterized by 92 clinical features. The framework incorporated seven feature ranking algorithms (LASSO, LightGBM, MCFS, mRMR, RF, CATBoost, and XGBoost), the IFS method, and four classification algorithms (DT, KNN, RF, and SVM). Results: Age, diabetes, dyspnea, chronic kidney failure, and high blood pressure were identified as the most important risk factors. The best model achieved an F1-score of 0.857 using KNN with 34 selected features. Conclusion: Our findings provide a comprehensive analysis of COVID-19 mortality risk factors and develops a robust predictive model. The findings highlight the increased risk in patients with comorbidities, consistent with existing literature. The proposed framework can aid in developing personalized treatment plans and allocating healthcare resources effectively.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881445","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}
Pub Date : 2024-08-02DOI: 10.2174/0115748936308564240712053215
Zhengfeng Wang, Xiujuan Lei, Yuchen Zhang, Fang-Xiang Wu, Yi Pan
The interaction between circular RNA (circRNA) and RNA binding protein (RBP) plays an important biological role in the occurrence and development of various diseases. Highthroughput biological experimental methods such as CLIP-seq can effectively analyze the interaction between the two, but biological experiments are inefficient and expensive, and they can only capture binding sites of a specific RBP on circRNA in a selected cell environment at a time. These biological experiments still rely on downstream data analysis to understand the mechanisms behind many biological structures and physiological processes. However, the rapid growth of experimental data dimensions and production speed pose challenges to traditional analysis methods. In recent years, deep learning has made great progress in the genome and transcriptome, and some deep learning prediction algorithms for RBP binding sites on circRNA have also emerged. In this paper, we briefly introduce some biological background knowledge related to circRNA-RBP interaction; present relevant deep learning techniques in this field, including the problem formulation, data source, sequence encoding, deep learning model and overall process of RBP binding sites prediction on circRNA; deeply analyze the current deep learning methods. Finally, some problems existing in the current research and the direction of future research are discussed. It is hoped to help researchers without basic knowledge of deep learning or basic biological background quickly understand the RBP binding sites prediction on circRNA.
{"title":"Recent Progress of Deep Learning Methods for RBP Binding Sites Prediction on circRNA","authors":"Zhengfeng Wang, Xiujuan Lei, Yuchen Zhang, Fang-Xiang Wu, Yi Pan","doi":"10.2174/0115748936308564240712053215","DOIUrl":"https://doi.org/10.2174/0115748936308564240712053215","url":null,"abstract":"The interaction between circular RNA (circRNA) and RNA binding protein (RBP) plays an important biological role in the occurrence and development of various diseases. Highthroughput biological experimental methods such as CLIP-seq can effectively analyze the interaction between the two, but biological experiments are inefficient and expensive, and they can only capture binding sites of a specific RBP on circRNA in a selected cell environment at a time. These biological experiments still rely on downstream data analysis to understand the mechanisms behind many biological structures and physiological processes. However, the rapid growth of experimental data dimensions and production speed pose challenges to traditional analysis methods. In recent years, deep learning has made great progress in the genome and transcriptome, and some deep learning prediction algorithms for RBP binding sites on circRNA have also emerged. In this paper, we briefly introduce some biological background knowledge related to circRNA-RBP interaction; present relevant deep learning techniques in this field, including the problem formulation, data source, sequence encoding, deep learning model and overall process of RBP binding sites prediction on circRNA; deeply analyze the current deep learning methods. Finally, some problems existing in the current research and the direction of future research are discussed. It is hoped to help researchers without basic knowledge of deep learning or basic biological background quickly understand the RBP binding sites prediction on circRNA.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881447","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}
Pub Date : 2024-08-02DOI: 10.2174/0115748936299878240723044438
Xin Zhang, Feiran Zhou, Pinglu Zhang, Quan Zou, Ying Zhang
Introduction: Traditional Chinese Medicine (TCM) has been extensively employed in the treatment of Monkeypox Virus (MPXV) infections, and it has historically played a significant role in combating diseases like contagious pox-like viral diseases in China. Method: Various traditional Chinese medicine (TCM) therapies have been recommended for patients with monkeypox virus (MPXV). However, as far as we know, there is no comprehensive database dedicated to preserving and coordinating TCM remedies for combating MPXV. To address this gap, we introduce TCM@MPXV, a carefully curated repository of research materials focusing on formulations with anti-MPXV properties. Importantly, TCM@MPXV extends its scope beyond herbal remedies, encompassing mineral-based medicines as well. Result: The current iteration of TCM@MPXV boasts an impressive array of features, including (1) Documenting over 42 types of TCM herbs, with more than 27 unique herbs; (2) Recording over 285 bioactivity compounds within these herbs; (3) Launching a user-friendly web server for the docking, analysis, and visualization of 2D or 3D molecular structures; and (4) Providing 3D structures of druggable proteins of MPXV. Conclusion: To summarize, TCM@MPXV presents a user-friendly and effective platform for recording, querying, and viewing anti-MPXV TCM resources and will contribute to the development and explanation of novel anti-MPXV mechanisms of action to aid in the ongoing battle against monkeypox. TCM@MPXV is accessible for academic use at http://101.34.238.132:5000/.
{"title":"TCM@MPXV: A Resource for Treating Monkeypox Patients in Traditional Chinese Medicine","authors":"Xin Zhang, Feiran Zhou, Pinglu Zhang, Quan Zou, Ying Zhang","doi":"10.2174/0115748936299878240723044438","DOIUrl":"https://doi.org/10.2174/0115748936299878240723044438","url":null,"abstract":"Introduction: Traditional Chinese Medicine (TCM) has been extensively employed in the treatment of Monkeypox Virus (MPXV) infections, and it has historically played a significant role in combating diseases like contagious pox-like viral diseases in China. Method: Various traditional Chinese medicine (TCM) therapies have been recommended for patients with monkeypox virus (MPXV). However, as far as we know, there is no comprehensive database dedicated to preserving and coordinating TCM remedies for combating MPXV. To address this gap, we introduce TCM@MPXV, a carefully curated repository of research materials focusing on formulations with anti-MPXV properties. Importantly, TCM@MPXV extends its scope beyond herbal remedies, encompassing mineral-based medicines as well. Result: The current iteration of TCM@MPXV boasts an impressive array of features, including (1) Documenting over 42 types of TCM herbs, with more than 27 unique herbs; (2) Recording over 285 bioactivity compounds within these herbs; (3) Launching a user-friendly web server for the docking, analysis, and visualization of 2D or 3D molecular structures; and (4) Providing 3D structures of druggable proteins of MPXV. Conclusion: To summarize, TCM@MPXV presents a user-friendly and effective platform for recording, querying, and viewing anti-MPXV TCM resources and will contribute to the development and explanation of novel anti-MPXV mechanisms of action to aid in the ongoing battle against monkeypox. TCM@MPXV is accessible for academic use at http://101.34.238.132:5000/.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881444","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}
Pub Date : 2024-07-29DOI: 10.2174/0115748936299440240709070105
Stephanie Chau, Carlos Rojas, Jorjeta G. Jetcheva, Mary Markart, Sudha Vijayakumar, Sophia Yuan, Vincent Stowbunenko, Amanda N. Shelton, William B. Andreopoulos
Background: Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. Methodology: More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-theart deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Result: Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools have the potential to improve identification performance relative to using the tools individually. Conclusion: We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content).
{"title":"Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics","authors":"Stephanie Chau, Carlos Rojas, Jorjeta G. Jetcheva, Mary Markart, Sudha Vijayakumar, Sophia Yuan, Vincent Stowbunenko, Amanda N. Shelton, William B. Andreopoulos","doi":"10.2174/0115748936299440240709070105","DOIUrl":"https://doi.org/10.2174/0115748936299440240709070105","url":null,"abstract":"Background: Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. Methodology: More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-theart deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Result: Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools have the potential to improve identification performance relative to using the tools individually. Conclusion: We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content).","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872100","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}
Background: Due to infection by the rice blast fungus, rice, a major global staple, faces yield challenges. While chemical control methods are common, their environmental and economic costs are growing concerns. Traditional biological experiments are also inefficient for exploring resistance genes. Therefore, understanding the interaction between rice and the rice blast fungus is urgent and important. Objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm to identify crucial crucial resistance factors.This could inform strategies for enhancing disease resistance in rice. objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm, to identify crucial crucial resistance factors. This could inform strategies for enhancing disease resistance in rice. Methods: We integrated genomics, transcriptomics, and proteomics data on Magnaporthe oryzae infecting rice. This multi-omics data was used to construct a multi-layer heterogeneous network.An advanced graph embedding algorithm (BINE) provided rich vector representations of network nodes. A multi-layer network walking algorithm was then used to analyze the network and identify key regulatory small RNA (sRNAs) in rice. Results: Node similarity rankings allowed us to identify significant regulatory sRNAs in rice that are integral to disease resistance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses further revealed their roles in biological processes and key metabolic pathways.Our integrative method precisely and efficiently identified these crucial elements, offering a valuable systems biology tool. Conclusion: By integrating multi-omics data with computational analysis, this study reveals key regulatory sRNAs in rice's disease resistance mechanism. These findings enhance our understanding of rice disease resistance and provide genetic resources for breeding disease-resistant rice. Despite limitations in sRNA functional interpretation, this research demonstrates the power of applying multi- omics data to address complex biological problems.
{"title":"A Novel Method for Mining Regulatory sRNAs Related to Rice Resistance Against Blast Fungus from Multi-Omics Data","authors":"Jianhua Sheng, Enshuang Zhao, Yuheng Zhu, Yinfei Dai, Borui Zhang, Qingming Qin, Hao Zhang","doi":"10.2174/0115748936305102240705052723","DOIUrl":"https://doi.org/10.2174/0115748936305102240705052723","url":null,"abstract":"Background: Due to infection by the rice blast fungus, rice, a major global staple, faces yield challenges. While chemical control methods are common, their environmental and economic costs are growing concerns. Traditional biological experiments are also inefficient for exploring resistance genes. Therefore, understanding the interaction between rice and the rice blast fungus is urgent and important. Objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm to identify crucial crucial resistance factors.This could inform strategies for enhancing disease resistance in rice. objective: This study aims to use multi-omics data to uncover key elements in rice's defense against rice blast fungus Magnaporthe oryzae. We built a detailed, multi-layered heterogeneous interaction network, employing an innovative graph embedding feature with a cross-layer random walk algorithm, to identify crucial crucial resistance factors. This could inform strategies for enhancing disease resistance in rice. Methods: We integrated genomics, transcriptomics, and proteomics data on Magnaporthe oryzae infecting rice. This multi-omics data was used to construct a multi-layer heterogeneous network.An advanced graph embedding algorithm (BINE) provided rich vector representations of network nodes. A multi-layer network walking algorithm was then used to analyze the network and identify key regulatory small RNA (sRNAs) in rice. Results: Node similarity rankings allowed us to identify significant regulatory sRNAs in rice that are integral to disease resistance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses further revealed their roles in biological processes and key metabolic pathways.Our integrative method precisely and efficiently identified these crucial elements, offering a valuable systems biology tool. Conclusion: By integrating multi-omics data with computational analysis, this study reveals key regulatory sRNAs in rice's disease resistance mechanism. These findings enhance our understanding of rice disease resistance and provide genetic resources for breeding disease-resistant rice. Despite limitations in sRNA functional interpretation, this research demonstrates the power of applying multi- omics data to address complex biological problems.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785583","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}
Pub Date : 2024-07-22DOI: 10.2174/0115748936290782240624114950
Chunliang Wang, Fanfan kong, Yu Wang, Hongjie Wu, Jun Yan
Background: In recent years, the rapid development of deep learning technology has had a significant impact on the prediction of DNA-binding proteins. Deep neural networks can automatically learn complex features in protein and DNA sequences, improving prediction accuracy and generalization capabilities. Objective: This article mainly establishes a meta-migration model and combines it with a deep learning model to predict DNA-binding proteins. Methods: This study introduces a meta-learning algorithm based on transfer learning, which helps achieve rapid learning and adaptation to new tasks. In addition, normalized Moreau-Broto autocorrelation attributes (NMBAC), position-specific scoring matrix-discrete cosine transform (PSSMDCT), and position-specific scoring matrix-discrete wavelet transform (PSSM-DWT) are also used for feature extraction. Finally, the prediction of DBP is achieved through the deep neural network model based on the attention mechanism. Results: This paper first establishes the basis of deep meta-transfer learning and uses the PDB186 data set as the benchmark to extract features using NMBAC, PSSM-DCT, and PSSM-DWT, respectively, and compare the fused features in pairs, and finally obtain the fused feature process. Through deep learning processing, it is concluded that the fused feature prediction effect is the best. At the same time, compared with the currently popular models, there are obvious improvements in the ACC, MCC, SN and Spec evaluation indicators. Conclusion: Finally, it was concluded that the method used in this article can effectively predict DNA-binding proteins and show more significant performance.
背景：近年来，深度学习技术的快速发展对 DNA 结合蛋白的预测产生了重大影响。深度神经网络可以自动学习蛋白质和 DNA 序列中的复杂特征，提高预测的准确性和泛化能力。目的：本文主要建立元迁移模型，并将其与深度学习模型相结合，预测DNA结合蛋白。方法：本研究引入了一种基于迁移学习的元学习算法，有助于实现快速学习和适应新任务。此外，归一化莫罗-布罗托自相关属性（NMBAC）、特定位置评分矩阵-离散余弦变换（PSSMDCT）和特定位置评分矩阵-离散小波变换（PSSM-DWT）也被用于特征提取。最后，通过基于注意力机制的深度神经网络模型实现对 DBP 的预测。结果本文首先建立了深度元转移学习的基础，并以 PDB186 数据集为基准，分别使用 NMBAC、PSSM-DCT 和 PSSM-DWT 提取特征，并对融合特征进行成对比较，最终得到融合特征过程。通过深度学习处理，得出融合特征预测效果最好的结论。同时，与目前流行的模型相比，在ACC、MCC、SN和Spec评价指标上都有明显改善。结论最后得出结论，本文所采用的方法能有效预测 DNA 结合蛋白，并表现出较为显著的性能。
{"title":"DNA Binding Protein Prediction based on Multi-feature Deep Metatransfer Learning","authors":"Chunliang Wang, Fanfan kong, Yu Wang, Hongjie Wu, Jun Yan","doi":"10.2174/0115748936290782240624114950","DOIUrl":"https://doi.org/10.2174/0115748936290782240624114950","url":null,"abstract":"Background: In recent years, the rapid development of deep learning technology has had a significant impact on the prediction of DNA-binding proteins. Deep neural networks can automatically learn complex features in protein and DNA sequences, improving prediction accuracy and generalization capabilities. Objective: This article mainly establishes a meta-migration model and combines it with a deep learning model to predict DNA-binding proteins. Methods: This study introduces a meta-learning algorithm based on transfer learning, which helps achieve rapid learning and adaptation to new tasks. In addition, normalized Moreau-Broto autocorrelation attributes (NMBAC), position-specific scoring matrix-discrete cosine transform (PSSMDCT), and position-specific scoring matrix-discrete wavelet transform (PSSM-DWT) are also used for feature extraction. Finally, the prediction of DBP is achieved through the deep neural network model based on the attention mechanism. Results: This paper first establishes the basis of deep meta-transfer learning and uses the PDB186 data set as the benchmark to extract features using NMBAC, PSSM-DCT, and PSSM-DWT, respectively, and compare the fused features in pairs, and finally obtain the fused feature process. Through deep learning processing, it is concluded that the fused feature prediction effect is the best. At the same time, compared with the currently popular models, there are obvious improvements in the ACC, MCC, SN and Spec evaluation indicators. Conclusion: Finally, it was concluded that the method used in this article can effectively predict DNA-binding proteins and show more significant performance.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753994","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}
Pub Date : 2024-07-22DOI: 10.2174/0115748936296412240625111040
Lichao Zhang, Kang Xiao, Xueting Wang, Liang Kong
Background: Small molecule-RNA binding sites play a significant role in developing drugs for disease treatment. However, it is a challenge to propose accurate computational tools for identifying these binding sites. Method: In this study, an accurate prediction model named CNRBind was constructed by extracting site significant information from nucleotide and complex networks. We designed complex networks and calculated three topological structural parameters according to RNA tertiary structure. Acknowledging nucleotide interdependence, a sliding window was selected to integrate the influence of adjacent sites. Finally, the model was constructed using a random forest classifier. Results: Compared to the other computational tools, CNRBind was competitive and had excellent discriminative ability for metal ion-binding site prediction. Furthermore, statistic analysis revealed significant differences between CNRBind and existing methods. Additionally, CNRBind is a promising predictor in cases where experimental tertiary structure is unavailable. Conclusion: These results show that CNRBind is effective because of the proposed site significant information encoding strategy. The approach provides a reasonable supplement for biology researches. The dataset and resource codes can be accessed at: https://github.com/Kangxiaoneuq/CNRBind.
{"title":"CNRBind: Small Molecule-RNA Binding Sites Recognition via Site Significant from Nucleotide and Complex Network Information","authors":"Lichao Zhang, Kang Xiao, Xueting Wang, Liang Kong","doi":"10.2174/0115748936296412240625111040","DOIUrl":"https://doi.org/10.2174/0115748936296412240625111040","url":null,"abstract":"Background: Small molecule-RNA binding sites play a significant role in developing drugs for disease treatment. However, it is a challenge to propose accurate computational tools for identifying these binding sites. Method: In this study, an accurate prediction model named CNRBind was constructed by extracting site significant information from nucleotide and complex networks. We designed complex networks and calculated three topological structural parameters according to RNA tertiary structure. Acknowledging nucleotide interdependence, a sliding window was selected to integrate the influence of adjacent sites. Finally, the model was constructed using a random forest classifier. Results: Compared to the other computational tools, CNRBind was competitive and had excellent discriminative ability for metal ion-binding site prediction. Furthermore, statistic analysis revealed significant differences between CNRBind and existing methods. Additionally, CNRBind is a promising predictor in cases where experimental tertiary structure is unavailable. Conclusion: These results show that CNRBind is effective because of the proposed site significant information encoding strategy. The approach provides a reasonable supplement for biology researches. The dataset and resource codes can be accessed at: https://github.com/Kangxiaoneuq/CNRBind.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753995","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}
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