Current Bioinformatics最新文献

{"title":"Investigation of LncRNAs Expression as a Potential Biomarker in the Diagnosis and Treatment of Human Brucellosis","authors":"Mansoor Kodori, Mohammad Abavisani, Hadis Fathizadeh, Mansoor Khaledi, Mohammad Hossein Haddadi, Shahrbanoo Keshavarz Aziziraftar, Foroogh Neamati, Amirhossein Sahebkar","doi":"10.2174/1574893618666230914160213","DOIUrl":"https://doi.org/10.2174/1574893618666230914160213","url":null,"abstract":"Abstract: Long non-coding RNAs (LncRNAs) are significant contributors to bacterial infections and host defense responses, presenting a novel class of gene regulators beyond conventional protein-coding genes. This narrative review aimed to explore the involvement of LncRNAs as a potential biomarker in the diagnosis and treatment of bacterial infections, with a specific focus on Brucella infections. A comprehensive literature review was conducted to identify relevant studies examining the roles of LncRNAs in immune responses during bacterial infections, with a specific emphasis on Brucella infections. PubMed, Scopus and other major scientific databases were searched using relevant keywords. LncRNAs crucially regulate immune responses to bacterial infections, influencing transcription factors, pro-inflammatory cytokines, and immune cell behavior, with both positive and negative effects. The NF-κB pathway is a key regulator for many LncRNAs in bacterial infections. During Brucella infections, essential LncRNAs activate the innate immune response, increasing proinflammatory cytokine production and immune cell differentiation. LncRNAs are associated with human brucellosis, holding promise for screening, diagnostics, or therapeutics. Further research is needed to fully understand LncRNAs' precise functions in Brucella infection and pathogenesis. Specific LncRNAs, like IFNG-AS1 and NLRP3, are upregulated during brucellosis, while others, such as Gm28309, are downregulated, influencing immunosuppression and bacterial survival. Investigating the prognostic and therapeutic potential of Brucella-related LncRNAs warrants ongoing investigation, including their roles in other immune cells like macrophages, dendritic cells, and neutrophils responsible for bacterial clearance. Unraveling the intricate relationship between LncRNAs and brucellosis may reveal novel regulatory mechanisms and LncRNAs' roles in infection regulation, expediting diagnostics and enhancing therapeutic strategies against Brucella infections.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134969910","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":"Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots","authors":"Xianzhe Zou, Chen Zhang, MIngyan Tang, Lei Deng","doi":"10.2174/1574893618666230913090436","DOIUrl":"https://doi.org/10.2174/1574893618666230913090436","url":null,"abstract":"Background: Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to be time-consuming and expensive, a growing number of machine learning techniques are being employed to predict hot spots. However, the existing approach is distinguished by a lack of uniform standards, a scarcity of data, and a wide range of attributes. Currently, there is no comprehensive overview or evaluation of this field. As a result, providing a full overview and review is extremely helpful. Methods: In this study, we present an overview of cutting-edge machine learning approaches utilized for hot spot prediction in protein-nucleic acid complexes. Additionally, we outline the feature categories currently in use, derived from relevant biological data sources, and assess conventional feature selection methods based on 600 extracted features. Simultaneously, we create two new benchmark datasets, PDHS87 and PRHS48, and develop distinct binary classification models based on these datasets to evaluate the advantages and disadvantages of various machine-learning techniques. Results: Prediction of protein-nucleic acid interaction hotspots is a challenging task. The study demonstrates that structural neighborhood features play a crucial role in identifying hot spots. The prediction performance can be improved by choosing effective feature selection methods and machine learning methods. Among the existing prediction methods, XGBPRH has the best performance. Conclusion: It is crucial to continue studying hot spot theories, discover new and effective features, add accurate experimental data, and utilize DNA/RNA information. Semi-supervised learning, transfer learning, and ensemble learning can optimize predictive ability. Combining computational docking with machine learning methods can potentially further improve predictive performance.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784172","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":"Drug-Target Interaction Prediction By Combining Transformer and Graph Neural Networks","authors":"Junkai Liu, Yaoyao Lu, Shixuan Guan, Tengsheng Jiang, Yijie Ding, Qiming Fu, Zhiming Cui, Hongjie Wu","doi":"10.2174/1574893618666230912141426","DOIUrl":"https://doi.org/10.2174/1574893618666230912141426","url":null,"abstract":"Background: The prediction of drug-target interactions (DTIs) plays an essential role in drug discovery. Recently, deep learning methods have been widely applied in DTI prediction. However, most of the existing research does not fully utilize the molecular structures of drug compounds and the sequence structures of proteins, which makes these models unable to obtain precise and effective feature representations. Methods: In this study, we propose a novel deep learning framework combining transformer and graph neural networks for predicting DTIs. Our model utilizes graph convolutional neural networks to capture the global and local structure information of drugs, and convolutional neural networks are employed to capture the sequence feature of targets. In addition, the obtained drug and protein representations are input to multi-layer transformer encoders, respectively, to integrate their features and generate final representations. Results: The experiments on benchmark datasets demonstrated that our model outperforms previous graph-based and transformer-based methods, with 1.5% and 1.8% improvement in precision and 0.2% and 1.0% improvement in recall, respectively. The results indicate that the transformer encoders effectively extract feature information of both drug compounds and proteins. Conclusion: Overall, our proposed method validates the applicability of combining graph neural networks and transformer architecture in drug discovery, and due to the attention mechanisms, it can extract deep structure feature data of drugs and proteins.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885613","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":"Prediction of Plant Ubiquitylation Proteins and Sites by Fusing Multiple Features","authors":"Meng-Yue Guan, Wang-Ren Qiu, Qian-Kun Wang, Xuan Xiao","doi":"10.2174/1574893618666230908092847","DOIUrl":"https://doi.org/10.2174/1574893618666230908092847","url":null,"abstract":"Introduction: Protein ubiquitylation is an important post-translational modification (PTM), which is considered to be one of the most important processes regulating cell function and various diseases. Therefore, accurate prediction of ubiquitylation proteins and their PTM sites is of great significance for the study of basic biological processes and the development of related drugs. Researchers have developed some large-scale computational methods to predict ubiquitylation sites, but there is still much room for improvement. Much of the research related to ubiquitylation is cross-species while the life pattern is diversified, and the prediction method always shows its specificity in practical application. This study just aims at the issue of plants and has constructed computational methods for identifying ubiquitylation protein and ubiquitylation sites. Method: In this work, we constructed two predictive models to identify plant ubiquitylation proteins and sites. First, in the ubiquitylation proteins prediction model, in order to better reflect protein sequence information and obtain better prediction results, the KNN scoring matrix model based on functional domain Gene Ontology (GO) annotation and word embedding model, i.e. Skip-Gram and Continuous Bag of Words (CBOW), are used to extract the features, and the light gradient boosting machine (LGBM) is selected as the ubiquitylation proteins prediction engine. Results: As a result, accuracy (ACC), Precision, recall rate (Recall), F1_score and AUC are respectively 85.12%, 80.96%, 72.80%, 76.37% and 0.9193 in the 10-fold cross-validations on independent dataset. In the ubiquitylation sites prediction model, Skip-Gram, CBOW and enhanced amino acid composition (EAAC) feature extraction codes were used to extract protein sequence fragment features, and the predicted results on training and independent test data have also achieved good performance. Conclusion: In a word, the comparison results demonstrate that our models have a decided advantage in predicting ubiquitylation proteins and sites, and it may provide useful insights for studying the mechanisms and modulation of ubiquitination pathways","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136361817","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 and Functional Prediction of lncRNAs using Bioinformatic Techniques","authors":"Shizuka Uchida","doi":"10.2174/1574893618666230907165829","DOIUrl":"https://doi.org/10.2174/1574893618666230907165829","url":null,"abstract":"<jats:sec>\u0000<jats:title />\u0000<jats:p />\u0000</jats:sec>","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135096561","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":"RDR100: an effective computational method for identifying Kruppel-like factors","authors":"Adeel Malik, Jamal S. M. Sabir, M. Kamli, Thi Phan Le, Chang-Bae Kim, Balachandran Manavalan","doi":"10.2174/1574893618666230905102407","DOIUrl":"https://doi.org/10.2174/1574893618666230905102407","url":null,"abstract":"\u0000\u0000Krüppel-like factors (KLFs) are a family of transcription factors containing zinc fingers that regulate various cellular processes. KLF proteins are associated with human diseases, such as cancer, cardiovascular diseases, and metabolic disorders. The KLF family consists of 18 members with diverse expression profiles across numerous tissues. Accurate identification and annotation of KLF proteins is crucial, given their involvement in important biological functions. Although experimental approaches can identify KLF proteins precisely, large-scale identification is complicated, slow, and expensive.\u0000\u0000\u0000\u0000In this study, we developed RDR100, a novel random forest (RF)-based framework for predicting KLF proteins based on their primary sequences. First, we identified the optimal encodings for ten different features using a recursive feature elimination approach, and then trained their respective model using five distinct machine learning (ML) classifiers. Results: The performance of all models was assessed using independent datasets, and RDR100 was selected as the final model based on its consistent performance in cross-validation and independent evaluation.\u0000\u0000\u0000\u0000Our results demonstrate that RDR100 is a robust predictor of KLF proteins. RDR100 web server is available at https://procarb.org/RDR100/.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48427754","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":"In Silico Study of Clinical Prognosis Associated MicroRNAs for patients with Metastasis in Clear Cell Renal Carcinoma","authors":"Ezra B. Wijaya, Venugopala Reddy Mekala, Efendi Zaenudin, Ka-Lok Ng","doi":"10.2174/1574893618666230905154441","DOIUrl":"https://doi.org/10.2174/1574893618666230905154441","url":null,"abstract":"Background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. Objective: This study aims to propose a computational research framework that hypothesizes that a set of miRNAs functions as key regulators in modulating gene expression networks of kidney cancer survival. Method: We retrieved the NGS data from the TCGA-KIRC extracted from UCSC Xena. A set of prognostic miRNAs was acquired through multiple Cox regression analyses. We adopted machine learning approaches to evaluate miRNA prognosis's classification performance between normal, primary (M0), and metastasis (M1) samples. The molecular mechanism between primary cancer and metastasis was investigated by identifying the regulatory networks of miRNA's target genes. Result: A total of 14 miRNAs were identified as potential prognostic indicators. A combination of high-expression miRNAs was associated with survival probability. Machine learning achieved an average accuracy of 95% in distinguishing primary cancer from normal tissue and 79% in predicting the metastasis from primary tissue. Correlation analysis of miRNA prognostics with target genes unveiled regulatory network disparities between metastatic and primary tissues. Conclusion: This study has identified 14 miRNAs that could potentially serve as vital biomarkers for diagnosing and prognosing ccRCC. Differential regulatory networks between metastatic and primary tissues in this study provide the molecular basis for assessment and therapeutic treatment for ccRCC patients","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135362419","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":"Transformer and graph transformer-based prediction of drug-target interactions","authors":"Weizhong Lu, Meiling Qian, Yu Zhang, Junkai Liu, Hongjie Wu, Yaoyao Lu, Haiou Li, Qiming Fu, Jiyun Shen, Yongbiao Xiao","doi":"10.2174/1574893618666230825121841","DOIUrl":"https://doi.org/10.2174/1574893618666230825121841","url":null,"abstract":"\u0000\u0000As we all know, finding new pharmaceuticals requires a lot of time and money, which has compelled people to think about adopting more effective approaches to locate drugs. Researchers have made significant progress recently when it comes to using Deep Learning (DL) to create DTI..\u0000\u0000\u0000\u0000Therefore, we propose a deep learning model that applies Transformer to DTI prediction. The model uses a Transformer and Graph Transformer to extract the feature information of protein and compound molecules, respectively, and combines their respective representations to predict interactions.\u0000\u0000\u0000\u0000We used Human and C.elegans, the two benchmark datasets, evaluated the proposed method in different experimental settings and compared it with the latest DL model.\u0000\u0000\u0000\u0000The results show that the proposed model based on DL is an effective method for the classification and recognition of DTI prediction, and its performance on the two data sets is significantly better than other DL based methods.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43591358","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":"DeepEpi: Deep Learning Model for Predicting Gene Expression Regulation based on Epigenetic Histone Modifications","authors":"Rania Hamdy, Yasser M. K. Omar, F. Maghraby","doi":"10.2174/1574893618666230818121046","DOIUrl":"https://doi.org/10.2174/1574893618666230818121046","url":null,"abstract":"\u0000\u0000Histone modification is a vital element in gene expression regulation. The way in which these proteins bind to the DNA impacts whether or not a gene may be expressed. Although those factors cannot in-fluence DNA construction, they can influence how it is transcribed.\u0000\u0000\u0000\u0000Each spatial location in DNA has its function, so the spatial ar-rangement of chromatin modifications affects how the gene can express. Al-so, gene regulation is affected by the type of histone modification combina-tions that are present on the gene and depends on the spatial distributional pattern of these modifications and how long these modifications read on a gene region. So, this study aims to know how to model Long-range spatial genome data and model complex dependencies among Histone reads.\u0000\u0000\u0000\u0000The Convolution Neural Network (CNN) is used to model all da-ta features in this paper. It can detect patterns in histones signals and pre-serve the spatial information of these patterns. It also uses the concept of memory in long short-term memory (LSTM), using vanilla LSTM, Bi-Directional LSTM, or Stacked LSTM to preserve long-range histones sig-nals. Additionally, it tries to combine these methods using ConvLSTM or uses them together with the aid of a self-attention.\u0000\u0000\u0000\u0000Based on the results, the combination of CNN, LSTM with the self-attention mechanism obtained an Area under the Curve (AUC) score of 88.87% over 56 cell types.\u0000\u0000\u0000\u0000The result outperforms the present state-of-the-art model and provides insight into how combinatorial interactions between histone modi-fication marks can control gene expression. The source code is available at https://github.com/RaniaHamdy/DeepEpi.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43840967","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":"scSDSC: Self-supervised Deep Subspace Clustering for scRNA-seq Data","authors":"Jian-ping Zhao, Bo Yang, Hai-yun Wang, Chunhan Zheng","doi":"10.2174/1574893618666230816090443","DOIUrl":"https://doi.org/10.2174/1574893618666230816090443","url":null,"abstract":"\u0000\u0000Single-cell RNA sequencing(scRNA-seq) data can identify heterogeneity between cells, thereby identifying cell types and discovering rare cell types. Clustering is often used to identify cell types, but the high noise and high dimension of scRNA-seq lead to the degradation of clustering performance and impact downstream analysis. Deep learning is widely used in this field, which provides promising performance in feature learning.\u0000\u0000\u0000\u0000Most deep learning models only consider the relationship between genes, ignore the relationship between cells. We try to use the relationships between cells and the relationships between genes to construct clustering models.\u0000\u0000\u0000\u0000We proposed scSDSC: a deep subspace cluster architecture that considers the relationships between genes and cells at the same time. Similar to deep subspace clustering (DSC), we added a fully connected layer after the embedding layer to obtain the self-expression matrix. In addition, we also added a fully connected SoftMax layer to generate the pseudo-label and used the information carried by the pseudo-label for model training. Finally, the affinity matrix is obtained for spectral clustering.\u0000\u0000\u0000\u0000Experimental results on eight real datasets show that scSDSC outperforms existing methods in downstream analysis.\u0000\u0000\u0000\u0000Our method plays an important role in improving clustering accuracy and downstream analysis.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43254950","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}