Current Bioinformatics最新文献

{"title":"Prediction of Drug Pathway-based Disease Classes using Multiple Properties of Drugs","authors":"Lei Chen, Linyang Li","doi":"10.2174/0115748936284973240105115444","DOIUrl":"https://doi.org/10.2174/0115748936284973240105115444","url":null,"abstract":"Background:: Drug repositioning now is an important research area in drug discovery as it can accelerate the procedures of discovering novel effects of existing drugs. However, it is challenging to screen out possible effects for given drugs. Designing computational methods are a quick and cheap way to complete this task. Most existing computational methods infer the relationships between drugs and diseases. The pathway-based disease classification reported in KEGG provides us a new way to investigate drug repositioning as such classification can be applied to drugs. A predicted class of a given drug suggests latent diseases it can treat. Objective:: The purpose of this study is to set up efficient multi-label classifiers to predict the classes of drugs. Method:: We adopt three types of drug information to generate drug features, including drug pathway information, label information and drug network. For the first two types, drugs are first encoded into binary vectors, which are further processed by singular value decomposition. For the third type, the network embedding algorithm, Mashup, is employed to yield drug features. Above features are combined and fed into RAndom k-labELsets (RAKEL) to construct multi-label classifiers, where support vector machine is selected as the base classification algorithm. Results:: The ten-fold cross-validation results show that the classifiers provide high performance with accuracy higher than 0.95 and absolute true higher than 0.92. The case study indicates the novel effects of three drugs, i.e., they may treat new diseases. Conclusion:: The proposed classifiers have high performance and are superiority to the classifiers with other classic algorithms and drug information. Furthermore, they have the ability to discover new effects of drugs.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587458","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}

{"title":"Prospects of Identifying Alternative Splicing Events from Single-Cell RNA Sequencing Data","authors":"Jiacheng Wang, Lei Yuan","doi":"10.2174/0115748936279561231214072041","DOIUrl":"https://doi.org/10.2174/0115748936279561231214072041","url":null,"abstract":"Background: The advent of single-cell RNA sequencing (scRNA-seq) technology has offered unprecedented opportunities to unravel cellular heterogeneity and functions. Yet, despite its success in unraveling gene expression heterogeneity, accurately identifying and interpreting alternative splicing events from scRNA-seq data remains a formidable challenge. With advancing technology and algorithmic innovations, the prospect of accurately identifying alternative splicing events from scRNA-seq data is becoming increasingly promising Objective: This perspective aims to uncover the intricacies of splicing at the single-cell level and their potential implications for health and disease. It seeks to harness scRNA-seq's transformative power in revealing cell-specific alternative splicing dynamics and aims to propel our understanding of gene regulation within individual cells to new heights. Methods: The perspective grounds its method on recent literature along with the experimental protocols of single-cell RNA-seq and methods to identify and quantify the alternative splicing events from scRNA-seq data. Results: This perspective outlines the promising potential, challenges, and methodologies for leveraging different scRNA-seq technologies to identify and study alternative splicing events, with a focus on advancing our understanding of gene regulation at the single-cell level. Conclusion: This perspective explores the prospects of utilizing scRNA-seq data to identify and study alternative splicing events, highlighting their potential, challenges, methodologies, biological insights, and future directions.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587677","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}

{"title":"Application of Deep Learning Neural Networks in Computer-aided Drug Discovery: A Review","authors":"Jay Shree Mathivanan, Victor Violet Dhayabaran, Mary Rajathei David, Muthugobal Bagayalakshmi Karuna Nidhi, Karuppasamy Muthuvel Prasath, Suvaiyarasan Suvaithenamudhan","doi":"10.2174/0115748936276510231123121404","DOIUrl":"https://doi.org/10.2174/0115748936276510231123121404","url":null,"abstract":": Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques to ameliorate the understanding of drug-target interactions in computer-aided drug discovery based on the prior knowledge acquired from various literature. In general, deep learning models can be trained to predict the binding affinity between the protein-ligand complexes and protein structures or generate protein-ligand complexes in structure-based drug discovery. In other words, artificial neural networks and deep learning algorithms, especially graph convolutional neural networks and generative adversarial networks, can be applied to drug discovery. Graph convolutional neural network effectively captures the interactions and structural information between atoms and molecules, which can be enforced to predict the binding affinity between protein and ligand. Also, the ligand molecules with the desired properties can be generated using generative adversarial networks.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587740","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}

{"title":"Integration of Artificial Intelligence, Machine Learning and Deep Learning Techniques in Genomics: Review on Computational Perspectives for NGS Analysis of DNA and RNA Seq Data","authors":"Chandrashekar K, Vidya Niranjan, Adarsh Vishal, Anagha S Setlur","doi":"10.2174/0115748936284044240108074937","DOIUrl":"https://doi.org/10.2174/0115748936284044240108074937","url":null,"abstract":": In the current state of genomics and biomedical research, the utilization of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) have emerged as paradigm shifters. While traditional NGS DNA and RNA sequencing analysis pipelines have been sound in decoding genetic information, the sequencing data’s volume and complexity have surged. There is a demand for more efficient and accurate methods of analysis. This has led to dependency on AI/ML and DL approaches. This paper highlights these tool approaches to ease combat the limitations and generate better results, with the help of pipeline automation and integration of these tools into the NGS DNA and RNA-seq pipeline we can improve the quality of research as large data sets can be processed using Deep Learning tools. Automation helps reduce labor-intensive tasks and helps researchers to focus on other frontiers of research. In the traditional pipeline all tasks from quality check to the variant identification in the case of SNP detection take a huge amount of computational time and manually the researcher has to input codes to prevent manual human errors, but with the power of automation, we can run the whole process in comparatively lesser time and smoother as the automated pipeline can run for multiple files instead of the one single file observed in the traditional pipeline. In conclusion, this review paper sheds light on the transformative impact of DL's integration into traditional pipelines and its role in optimizing computational time. Additionally, it highlights the growing importance of AI-driven solutions in advancing genomics research and enabling data-intensive biomedical applications.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139553927","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}

{"title":"Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm","authors":"Yaxuan Cui, Leyi Wei, Ruheng Wang, Xiucai Ye, Tetsuya Sakurai","doi":"10.2174/0115748936278884240102094058","DOIUrl":"https://doi.org/10.2174/0115748936278884240102094058","url":null,"abstract":"Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial information effectively and are highly sensitive to noise and outliers. In this study, we propose ACSpot, an autoencoder-based fuzzy clustering algorithm, as a solution to tackle these problems. Specifically, we employed a self-supervised autoencoder to reduce feature dimensionality, mitigate nonlinear noise, and learn high-quality representations. Additionally, a commonly used clustering method, Fuzzy c-means, is used to achieve improved clustering results. In particular, we utilize spatial neighbor information to optimize the clustering process and to fine-tune each spot to its associated cluster category using probabilistic and statistical methods. Result and Discussion: The comparative analysis on the 10x Visium human dorsolateral prefrontal cortex (DLPFC) dataset demonstrates that ACSpot outperforms other clustering algorithms. Subsequently, spatially variable genes were identified based on the clustering outcomes, revealing a striking similarity between their spatial distribution and the subcluster spatial distribution from the clustering results. Notably, these spatially variable genes include APP, PSEN1, APOE, SORL1, BIN1, and PICALM, all of which are well-known Alzheimer's disease-associated genes. Conclusion: In addition, we applied our model to explore some potential Alzheimer's disease correlated genes within the dataset and performed Gene Ontology (GO) enrichment and gene-pathway analyses for validation, illustrating the capability of our model to pinpoint genes linked to Alzheimer’s disease.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139518523","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}

{"title":"Identification of Mitophagy-Related Genes in Sepsis","authors":"Xiao-Yan Zeng, Min Zhang, Si-Jing Liao, Yong Wang, Ying-Bo Ren, Run Li, Tian-Mei Li, An-Qiong Mao, Guang-Zhen Li, Ying Zhang","doi":"10.2174/0115748936266722231116050255","DOIUrl":"https://doi.org/10.2174/0115748936266722231116050255","url":null,"abstract":"Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. background: Sepsis is a critical systemic infection, a syndrome of severe inflammatory response of the organism to various pathogenic microorganisms. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. objective: In this study, we used a bioinformatics approach to integrate multiple microarray data to screen for mitochondrial autophagy-related hub genes associated with sepsis onset and progression in a more scientific and systematic manner. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. method: Robust rank aggregation (RRA) Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies. result: we constructed a PPI network combined with RRA analysis method to finally identify 4 key genes, namely TOMM20, TOMM22, TOMM40, and MFN1. conclusion: In this study, we used a bioinformatics analysis method, RRA, to integrate five gene microarray datasets to identify pivotal genes associated with mitochondrial autophagy in sepsis. Gene ontology (GO) functional annotation results show that these hub genes are mainly enriched in mitochondrial transport and establishment of protein localization to mitochondrion. Finally, we constructed the PPI network with the top 100 genes obtained from the rra method analysis. Based on the RRA results, the PPI results and the mitochondrial autophagy-related genes we found in the Reactome Pathway Database, we finally identified four key genes as TOMM20, TOMM22, TOMM40, and MFN1, respectively.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376298","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}

{"title":"Translation of Circular RNAs: Functions of Translated Products and Related Bioinformatics Approaches.","authors":"Jae Yeon Hwang, Tae Lim Kook, Sydney M Paulus, Juw Won Park","doi":"10.2174/1574893618666230505101059","DOIUrl":"10.2174/1574893618666230505101059","url":null,"abstract":"<p><p>Over the past two decades, studies have discovered a special form of alternative splicing (AS) that produces a circular form of RNA. This stands in contrast to normal AS, which produces a linear form of RNA. Although these circRNAs have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of these studies has been on the regulatory role of circRNAs with the assumption that circRNAs are non-coding. As non-coding RNAs, they may regulate mRNA transcription, tumor initiation, and translation by sponging miRNAs and RNA-binding proteins (RBPs). In addition to these regulatory roles of circRNAs, however, recent studies have provided strong evidence for their translation. The translation of circRNAs is expected to have an important role in promoting cancer cell growth and activating molecular pathways related to cancer development. In some cases, the translation of circRNAs is shown to be efficiently driven by an internal ribosome entry site (IRES). The development of a computational tool for identifying and characterizing the translation of circRNAs using high-throughput sequencing and IRES increases identifiable proteins translated from circRNAs. In turn, it has a substantial impact on helping researchers understand the functional role of proteins derived from circRNAs. New web resources for aggregating, cataloging, and visualizing translational information of circRNAs derived from previous studies have been developed. In this paper, general concepts of circRNA, circRNA biogenesis, translation of circRNA, and existing circRNA tools and databases are summarized to provide new insight into circRNA studies.</p>","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10947221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47470109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Natural Graph for Efficient Clustering of Virus Genome Sequences","authors":"Harris Song, Nan Sun, Wenping Yu, Stephen Yau","doi":"10.2174/0115748936269106231025064143","DOIUrl":"https://doi.org/10.2174/0115748936269106231025064143","url":null,"abstract":"Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades, subtypes, or districts. Additionally, the aim is to explore its interpretability, potential applications, and implications for pandemic control and public health interventions. Methods: The study utilizes the proposed natural graph algorithm to cluster viral genome sequences. The results are compared with existing methods and multidimensional scaling to evaluate the performance and effectiveness of the approach. Results: The natural graph approach successfully clusters viral genome sequences, providing valuable insights into viral evolution and transmission dynamics. The ability to generate directed connections between nodes enhances the interpretability of the results, facilitating the investigation of transmission pathways and viral fitness. Conclusion: The findings highlight the potential applications of the natural graph algorithm in pandemic control, transmission tracing, and vaccine design. Future research directions may involve scaling up the analysis to larger datasets and incorporating additional genetic features for improved resolution. The natural graph approach presents a promising tool for viral genomics research with implications for public health interventions.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138687256","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}

{"title":"Integrating Single-cell and Bulk RNA Sequencing Reveals Stemness Phenotype Associated with Clinical Outcomes and Potential Immune Evasion Mechanisms in Hepatocellular Carcinoma","authors":"Xiaojing Zhu, Jiaxing Zhang, Zixin Zhang, Hongyan Yuan, Aimin xie, Nan Zhang, Mingwei wang, Minghui jiang, Yanqi Xiao, Hao Wang, Xing Wang, Yan Xu","doi":"10.2174/0115748936268168231114103440","DOIUrl":"https://doi.org/10.2174/0115748936268168231114103440","url":null,"abstract":"Aims: Bulk and single-cell RNA sequencing data were analyzed to explore the association of stemness phenotype with dysfunctional anti-tumor immunity and its impact on clinical outcomes of primary and relapse HCC. Background: The stemness phenotype is gradually acquired during cancer progression; however, it remains unclear the effect of stemness phenotype on recurrence and clinical outcomes in hepatocellular carcinoma (HCC). Methods: The stemness index (mRNAsi) calculated by a one-class logistic regression algorithm in multiple HCC cohorts was defined as the stemness phenotype of the patient. Using single-cell profiling in primary or early-relapse HCC, cell stemness phenotypes were evaluated by developmental potential. Differential analysis of stemness phenotype, gene expression and interactions between primary and recurrent samples revealed the underlying immune evasion mechanisms. Results: A significant mRNAsi association with HCC patient clinical outcomes was found. The high and low mRNAsi groups had distinct tumor immune microenvironments. Cellular stemness phenotype varied by cell type. Moreover, compared with primary tumors, early-relapse tumors had increased stemness of dendritic cells and tumor cells and reduced stemness of T cells and B cells. Moreover, in relapse tumors, CD8+ T cells displayed a low stemness state, with a high exhausted state, unlike the high stemness state observed in primary HCC. Conclusions: The comprehensive characterization of the HCC stemness phenotype provides insights into the clinical outcomes and immune escape mechanisms associated with recurrence.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138686838","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}

{"title":"A Novel In silico Filtration Method for Discovery of Encrypted Antimicrobial Peptides","authors":"Farnoosh Barneh, Ahmad Nazarian, Rezvan Mousavi-nadushan, Kamran Pooshang Bagheri","doi":"10.2174/0115748936274103231114105340","DOIUrl":"https://doi.org/10.2174/0115748936274103231114105340","url":null,"abstract":"Background:: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them. Objective:: In this study, we proposed a novel filtration method using knowledge-based approaches to discover encrypted AMPs within a protein sequence Methods:: The encrypted AMPs were selected from a protein sequence, in this case, lactoferrin, based on hydrophobicity, cationicity, alpha-helix structure, helical wheel projection, and binding affinities to gram-negative and positive bacterial membranes. Results:: Six out of 20 potential encrypted AMPs were ultimately selected for further assays. Molecular docking of the selected AMPs with outer and inner membranes of gram-negative bacteria and also gram-positive bacterial membranes showed reasonable binding affinity ranging from ‘-6.7 to -7.5’ and ‘- 4.5 to -5.7’ and ‘-4.6 to -5.7’ kcal/mol, respectively. No toxicity was shown in the candidate AMPs. Conclusion:: According to in silico results, our method succeeded to discover six new encrypted AMPs from human lactoferrin, designated as lactoferrin-derived peptides (LDPs). Further in silico and experimental assays should also be performed to prove the efficiency of our knowledge-based filtration method.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138555680","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}