Pub Date : 2023-07-31DOI: 10.2174/1574893618666230731164002
G. S, Keshav T R, R. H, Fayaz Sm
Intricacy in the pathological processes of type 2 diabetes (T2D) invites a need to understand gene regulation at the systems level. However, deciphering the complex gene modulation requires regulatory network construction. The study aims to construct a six-node feed-forward loop (FFL) to analyze all the diverse inter- and intra- interactions between microRNAs (miRNA) and transcription factors (TF) involved in gene regulation. The study included 644 genes, 64 TF, and 448 miRNA. A cumulative hypergeometric test was employed to identify the significant miRNA-miRNA and miRNA-TF interaction pairs. In addition, experimentally proven TF-TF pairs were incorporated for the first time in the regulatory network to discern gene regulation. The networks were analyzed to identify crucial genes involved in T2D. Following this, gene ontology was predicted to recognize the biological function that is crucial in T2D. In T2D, the lowest gene regulation for a composite FFL occurs through a four-node FFL variant1 (TF- miRNA-miRNA-Gene, n=14) and the highest regulation via a five-node FFL variant2 (TF-TF-miRNA-Gene, n=353). However, the maximum gene regulation occurs via six-node miRNA FFL (miRNA-miRNA-TF-TF-gene-gene, n=23987). Subnetworks derived from the six-node miRNA-TF-gene regulatory networks identified interactions among TP53 and NFkB, hsa-miR-125-5p and hsa-miR-155-5p. The core regulation occurs through TP53, NFkB, hsa-miR-125-5p, and hsa-miR-155-5p FFL implicating the association of inflammation in the pathogenesis of T2D, which occurs majorly via six-node miRNA FFL. Thus regulatory network provides broader insights into the pathogenesis of T2D and can be extended to study the inflammatory mechanisms in various infections.
{"title":"Interplay Of miRNA-TF-Gene Through A Novel Six-Node Feed-Forward Loop Identified Inflammatory Genes As Key Regulators In Type-2 Diabetes","authors":"G. S, Keshav T R, R. H, Fayaz Sm","doi":"10.2174/1574893618666230731164002","DOIUrl":"https://doi.org/10.2174/1574893618666230731164002","url":null,"abstract":"\u0000\u0000Intricacy in the pathological processes of type 2 diabetes (T2D) invites a need to understand gene regulation at the systems level. However, deciphering the complex gene modulation requires regulatory network construction.\u0000\u0000\u0000\u0000The study aims to construct a six-node feed-forward loop (FFL) to analyze all the diverse inter- and intra- interactions between microRNAs (miRNA) and transcription factors (TF) involved in gene regulation.\u0000\u0000\u0000\u0000The study included 644 genes, 64 TF, and 448 miRNA. A cumulative hypergeometric test was employed to identify the significant miRNA-miRNA and miRNA-TF interaction pairs. In addition, experimentally proven TF-TF pairs were incorporated for the first time in the regulatory network to discern gene regulation. The networks were analyzed to identify crucial genes involved in T2D. Following this, gene ontology was predicted to recognize the biological function that is crucial in T2D.\u0000\u0000\u0000\u0000In T2D, the lowest gene regulation for a composite FFL occurs through a four-node FFL variant1 (TF- miRNA-miRNA-Gene, n=14) and the highest regulation via a five-node FFL variant2 (TF-TF-miRNA-Gene, n=353). However, the maximum gene regulation occurs via six-node miRNA FFL (miRNA-miRNA-TF-TF-gene-gene, n=23987). Subnetworks derived from the six-node miRNA-TF-gene regulatory networks identified interactions among TP53 and NFkB, hsa-miR-125-5p and hsa-miR-155-5p.\u0000\u0000\u0000\u0000The core regulation occurs through TP53, NFkB, hsa-miR-125-5p, and hsa-miR-155-5p FFL implicating the association of inflammation in the pathogenesis of T2D, which occurs majorly via six-node miRNA FFL. Thus regulatory network provides broader insights into the pathogenesis of T2D and can be extended to study the inflammatory mechanisms in various infections.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41709390","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}
The COVID-19 pandemic has affected every country and changed people's lives. Accurate prediction of COVID-19 trends can help prevent the further spread of the outbreak. However, the changing environment affects the COVID-19 prediction performance, and previous models are limited in practical applications. An explainable multichannel deep learning model with spatial, temporal and environmental channels for time series prediction, STE-COVIDNet, was proposed. The time series data of COVID-19 infection, weather, in-state population mobility, and vaccination were collected from May, 2020, to October, 2021, in the USA. In the environmental channel of STE-COVIDNet, an attention mechanism was applied to extract significant environmental factors related to the spread of COVID-19. In addition, the attention weights of these factors were analyzed with the actual situation. STE-COVIDNet was found to be superior to other advanced prediction models of COVID-19 infection cases. The analysis results of attention weight were reported to be consistent with existing studies and reports. It was found that the same environmental factors that influence the spread of COVID-19 can vary across time and region, which explains why findings of previous studies on the relationship between the environment and COVID-19 vary by region and time. STE-COVIDNet is an explainable model that can adapt to environmental changes and thus improve predictive performance.
{"title":"An Explainable Multichannel Model for COVID-19 Time Series Prediction","authors":"Hongjiang He, Jiang Xie, Xinwei Lu, Dingkai Huang, Wenjun Zhang","doi":"10.2174/1574893618666230727160507","DOIUrl":"https://doi.org/10.2174/1574893618666230727160507","url":null,"abstract":"\u0000\u0000The COVID-19 pandemic has affected every country and changed people's lives. Accurate prediction of COVID-19 trends can help prevent the further spread of the outbreak. However, the changing environment affects the COVID-19 prediction performance, and previous models are limited in practical applications.\u0000\u0000\u0000\u0000An explainable multichannel deep learning model with spatial, temporal and environmental channels for time series prediction, STE-COVIDNet, was proposed. The time series data of COVID-19 infection, weather, in-state population mobility, and vaccination were collected from May, 2020, to October, 2021, in the USA. In the environmental channel of STE-COVIDNet, an attention mechanism was applied to extract significant environmental factors related to the spread of COVID-19. In addition, the attention weights of these factors were analyzed with the actual situation.\u0000\u0000\u0000\u0000STE-COVIDNet was found to be superior to other advanced prediction models of COVID-19 infection cases. The analysis results of attention weight were reported to be consistent with existing studies and reports. It was found that the same environmental factors that influence the spread of COVID-19 can vary across time and region, which explains why findings of previous studies on the relationship between the environment and COVID-19 vary by region and time.\u0000\u0000\u0000\u0000STE-COVIDNet is an explainable model that can adapt to environmental changes and thus improve predictive performance.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44189539","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 : 2023-07-27DOI: 10.2174/1574893618666230727103257
M. Aikawa, P. Jha, Miguel Barbeiro, A. Lupieri, E. Aikawa, S. Uchida
Treatment of many human diseases involves small-molecule drugs.Some target proteins, however, are not druggable with traditional strategies. Innovative RNA-targeted therapeutics may overcome such a challenge. Long noncoding RNAs (lncRNAs) are transcribed RNAs that do not translate into proteins. Their ability to interact with DNA, RNA, microRNAs (miRNAs), and proteins makes them an interesting target for regulating gene expression and signaling pathways.In the past decade, a catalog of lncRNAs has been studied in several human diseases. One of the challenges with lncRNA studies include their lack of coding potential, making, it difficult to characterize them in wet-lab experiments functionally. Several computational tools have thus been designed to characterize functions of lncRNAs centered aroundlncRNA interaction with proteins and RNA, especially miRNAs. This review comprehensively summarizes the methods and tools for lncRNA-RNA interactions and lncRNA-protein interaction prediction.We discuss the tools related to lncRNA interaction prediction using commonlyused models: ensemble-based, machine-learning-based, molecular-docking and network-based computational models. In biology, two or more genes co-expressed tend to have similar functions. Coexpression network analysis is, therefore, one of the most widely-used methods for understanding the function of lncRNAs. A major focus of our study is to compile literature related to the functional prediction of lncRNAs in human diseases using co-expression network analysis. In summary, this article provides relevant information on the use of appropriate computational tools for the functional characterization of lncRNAs that help wet-lab researchers design mechanistic and functional experiments.
{"title":"Computational methods for functional characterization of lncRNAs in human diseases: A focus on co-expression networks","authors":"M. Aikawa, P. Jha, Miguel Barbeiro, A. Lupieri, E. Aikawa, S. Uchida","doi":"10.2174/1574893618666230727103257","DOIUrl":"https://doi.org/10.2174/1574893618666230727103257","url":null,"abstract":"Treatment of many human diseases involves small-molecule drugs.Some target proteins,\u0000however, are not druggable with traditional strategies. Innovative RNA-targeted therapeutics may overcome such a challenge. Long noncoding RNAs (lncRNAs) are transcribed RNAs that do not translate\u0000into proteins. Their ability to interact with DNA, RNA, microRNAs (miRNAs), and proteins makes\u0000them an interesting target for regulating gene expression and signaling pathways.In the past decade, a\u0000catalog of lncRNAs has been studied in several human diseases. One of the challenges with lncRNA\u0000studies include their lack of coding potential, making, it difficult to characterize them in wet-lab experiments functionally. Several computational tools have thus been designed to characterize functions of\u0000lncRNAs centered aroundlncRNA interaction with proteins and RNA, especially miRNAs. This review\u0000comprehensively summarizes the methods and tools for lncRNA-RNA interactions and lncRNA-protein\u0000interaction prediction.We discuss the tools related to lncRNA interaction prediction using commonlyused models: ensemble-based, machine-learning-based, molecular-docking and network-based computational models. In biology, two or more genes co-expressed tend to have similar functions. Coexpression network analysis is, therefore, one of the most widely-used methods for understanding the\u0000function of lncRNAs. A major focus of our study is to compile literature related to the functional prediction of lncRNAs in human diseases using co-expression network analysis. In summary, this article provides relevant information on the use of appropriate computational tools for the functional characterization of lncRNAs that help wet-lab researchers design mechanistic and functional experiments.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42661014","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 : 2023-07-25DOI: 10.2174/1574893618666230725123211
N. Sreekumar, Suvanish Kumar Valsala Sudarsanan
Genome assembly and annotation are crucial steps in plant genomics research as they provide valuable insights into plant genetic makeup, gene regulation, evolutionary history, and biological processes. In the emergence of high-throughput sequencing technologies, a plethora of genome assembly tools have been developed to meet the diverse needs of plant genome researchers. Choosing the most suitable tool to suit a specific research need can be daunting due to the complex and varied nature of plant genomes and reads from the sequencers. To assist informed decision-making in selecting the appropriate genome assembly and annotation tool(s), this review offers an extensive overview of the most widely used genome and transcriptome assembly tools. The review covers the specific information on each tool in tabular data, and the data types it can process. In addition, the review delves into transcriptome assembly tools, plant resource databases, and repositories (12 for Arabidopsis, 9 for Rice, 5 for Tomato, and 8 general use resources), which are vital for gene expression profiling and functional annotation and ontology tools that facilitate data integration and analysis.
{"title":"Bioinformatic Resources for Plant Genomic Research","authors":"N. Sreekumar, Suvanish Kumar Valsala Sudarsanan","doi":"10.2174/1574893618666230725123211","DOIUrl":"https://doi.org/10.2174/1574893618666230725123211","url":null,"abstract":"\u0000\u0000Genome assembly and annotation are crucial steps in plant genomics research as they provide valuable insights into plant genetic makeup, gene regulation, evolutionary history, and biological processes. In the emergence of high-throughput sequencing technologies, a plethora of genome assembly tools have been developed to meet the diverse needs of plant genome researchers. Choosing the most suitable tool to suit a specific research need can be daunting due to the complex and varied nature of plant genomes and reads from the sequencers. To assist informed decision-making in selecting the appropriate genome assembly and annotation tool(s), this review offers an extensive overview of the most widely used genome and transcriptome assembly tools. The review covers the specific information on each tool in tabular data, and the data types it can process. In addition, the review delves into transcriptome assembly tools, plant resource databases, and repositories (12 for Arabidopsis, 9 for Rice, 5 for Tomato, and 8 general use resources), which are vital for gene expression profiling and functional annotation and ontology tools that facilitate data integration and analysis.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47212631","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}
Prostate cancer (PCa) is a kind of malignant tumor with high incidence among males worldwide. The identification of novel biomarker signatures is, therefore of clinical significance for PCa precision medicine. It has been acknowledged that the breaking of stability and vulnerability in biological network provides important clues for cancer biomarker discovery. In this study, a bioinformatics model by characterizing the centrality of nodes in PCa-specific protein-protein interaction (PPI) network was proposed and applied to identify novel gene signatures for PCa detection. Compared with traditional methods, this model integrated degree, closeness and betweenness centrality as the criterion for Hub gene prioritization. The identified biomarkers were validated based on receiver-operating characteristic evaluation, qRT-PCR experimental analysis and literature-guided functional survey. Four genes, i.e., MYOF, RBFOX3, OCLN, and CDKN1C, were screened with average AUC ranging from 0.79 to 0.87 in the predicted and validated datasets for PCa diagnosis. Among them, MYOF, RBFOX3, and CDKN1C were observed to be down-regulated whereas OCLN was over-expressed in PCa groups. The in vitro qRT-PCR experiment using cell line samples convinced the potential of identified genes as novel biomarkers for PCa detection. Biological process and pathway enrichment analysis suggested the underlying role of identified biomarkers in mediating PCa-related genes and pathways including TGF-β, Hippo, MAPK signaling during PCa occurrence and progression. Novel gene signatures were screened as candidate biomarkers for PCa detection based on topological characterization of PCa-specific PPI network. More clinical validation using human samples will be performed in future work.
{"title":"Novel Gene Signatures for Prostate Cancer Detection: Network Centrality-based Screening with Experimental Validation","authors":"Jianquan Hou, Yuxin Lin, Anguo Zhao, Xuefeng Zhang, Guang Hu, Xue-dong Wei, Yuhua Huang","doi":"10.2174/1574893618666230713155145","DOIUrl":"https://doi.org/10.2174/1574893618666230713155145","url":null,"abstract":"\u0000\u0000Prostate cancer (PCa) is a kind of malignant tumor with high incidence among males worldwide. The identification of novel biomarker signatures is, therefore of clinical significance for PCa precision medicine. It has been acknowledged that the breaking of stability and vulnerability in biological network provides important clues for cancer biomarker discovery.\u0000\u0000\u0000\u0000In this study, a bioinformatics model by characterizing the centrality of nodes in PCa-specific protein-protein interaction (PPI) network was proposed and applied to identify novel gene signatures for PCa detection. Compared with traditional methods, this model integrated degree, closeness and betweenness centrality as the criterion for Hub gene prioritization. The identified biomarkers were validated based on receiver-operating characteristic evaluation, qRT-PCR experimental analysis and literature-guided functional survey.\u0000\u0000\u0000\u0000Four genes, i.e., MYOF, RBFOX3, OCLN, and CDKN1C, were screened with average AUC ranging from 0.79 to 0.87 in the predicted and validated datasets for PCa diagnosis. Among them, MYOF, RBFOX3, and CDKN1C were observed to be down-regulated whereas OCLN was over-expressed in PCa groups. The in vitro qRT-PCR experiment using cell line samples convinced the potential of identified genes as novel biomarkers for PCa detection. Biological process and pathway enrichment analysis suggested the underlying role of identified biomarkers in mediating PCa-related genes and pathways including TGF-β, Hippo, MAPK signaling during PCa occurrence and progression.\u0000\u0000\u0000\u0000Novel gene signatures were screened as candidate biomarkers for PCa detection based on topological characterization of PCa-specific PPI network. More clinical validation using human samples will be performed in future work.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41536995","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 : 2023-07-10DOI: 10.2174/1574893618666230710140505
R. Qiu, Danlei Ru, Jinchen Li, Linliu Peng, Hong Jiang
Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is a clinically heterogeneous and progressive condition. Evaluation of its progression will contribute to clinical management and genetic counseling. The objective of this study was to provide a visualized interpretable prediction of the progression of SCA3/MJD based on machine learning (ML) methods. A total of 716 patients with SCA3/MJD were included in this study. The International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA) scores were used to quantitatively assess disease progression in the patients. Clinical and genotype information were collected as factors for predicting progression. Prediction models were constructed with ML algorithms, and the prediction results were then visualized to facilitate personalizing of clinical consultation. The CAG repeat length of ATXN3 and its product with age, the duration of disease, and age were identified as the 4 most important factors for predicting the severity and progression of SCA3/MJD. The SVM-based model achieved the best performance in predicting the total ICARS and SARA scores, with accuracy (10%) values of 0.7619 for the SARA and 0.7042 for the ICARS. To visualize the predictions, line charts were used to show the expected progression over the next decade, and radar charts were used to show the scores of each part of the ICARS and SARA separately. We are the first group to apply ML algorithms to predict progression in SCA3/MJD and achieved desirable results. Visualization provided personalized predictions for each sample and can aid in developing clinical counseling regimens in the future.
{"title":"Visual Prediction of the Progression of Spinocerebellar Ataxia Type 3 Based on Machine Learning","authors":"R. Qiu, Danlei Ru, Jinchen Li, Linliu Peng, Hong Jiang","doi":"10.2174/1574893618666230710140505","DOIUrl":"https://doi.org/10.2174/1574893618666230710140505","url":null,"abstract":"\u0000\u0000Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is a clinically heterogeneous and progressive condition. Evaluation of its progression will contribute to clinical management and genetic counseling.\u0000\u0000\u0000\u0000The objective of this study was to provide a visualized interpretable prediction of the progression of SCA3/MJD based on machine learning (ML) methods.\u0000\u0000\u0000\u0000A total of 716 patients with SCA3/MJD were included in this study. The International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA) scores were used to quantitatively assess disease progression in the patients. Clinical and genotype information were collected as factors for predicting progression. Prediction models were constructed with ML algorithms, and the prediction results were then visualized to facilitate personalizing of clinical consultation.\u0000\u0000\u0000\u0000The CAG repeat length of ATXN3 and its product with age, the duration of disease, and age were identified as the 4 most important factors for predicting the severity and progression of SCA3/MJD. The SVM-based model achieved the best performance in predicting the total ICARS and SARA scores, with accuracy (10%) values of 0.7619 for the SARA and 0.7042 for the ICARS. To visualize the predictions, line charts were used to show the expected progression over the next decade, and radar charts were used to show the scores of each part of the ICARS and SARA separately.\u0000\u0000\u0000\u0000We are the first group to apply ML algorithms to predict progression in SCA3/MJD and achieved desirable results. Visualization provided personalized predictions for each sample and can aid in developing clinical counseling regimens in the future.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43770792","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 : 2023-07-07DOI: 10.2174/1574893618666230707103956
S. Uchida, Rebecca Distefano, Mirolyuba Ilieva, Sarah Rennie
Long non-coding RNAs (lncRNAs) typically refer to non-protein coding RNAs that are longer than 200 nucleotides. Historically dismissed as junk DNA, over two decades of research have revealed that lncRNAs bind to other macromolecules (e.g., DNA, RNA, and/or proteins) to modulate signaling pathways and maintain organism viability. Their discovery has been significantly aided by the development of bioinformatics tools in recent years. However, the diversity of tools for lncRNA discovery and functional prediction can confuse researchers, especially bench scientists and clinicians. This Perspective article aims to navigate the current landscape of bioinformatic tools suitable for both protein-coding and lncRNA genes. It aims to provide a guide for bench scientists and clinicians to select the appropriate tools for their research questions and experimental designs.
{"title":"Recommendations for Bioinformatic Tools in LncRNA Research","authors":"S. Uchida, Rebecca Distefano, Mirolyuba Ilieva, Sarah Rennie","doi":"10.2174/1574893618666230707103956","DOIUrl":"https://doi.org/10.2174/1574893618666230707103956","url":null,"abstract":"\u0000\u0000Long non-coding RNAs (lncRNAs) typically refer to non-protein coding RNAs that are longer than 200 nucleotides. Historically dismissed as junk DNA, over two decades of research have revealed that lncRNAs bind to other macromolecules (e.g., DNA, RNA, and/or proteins) to modulate signaling pathways and maintain organism viability. Their discovery has been significantly aided by the development of bioinformatics tools in recent years. However, the diversity of tools for lncRNA discovery and functional prediction can confuse researchers, especially bench scientists and clinicians. This Perspective article aims to navigate the current landscape of bioinformatic tools suitable for both protein-coding and lncRNA genes. It aims to provide a guide for bench scientists and clinicians to select the appropriate tools for their research questions and experimental designs.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43218910","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 : 2023-07-07DOI: 10.2174/1574893618666230707123817
Xiu-juan Lei, Mei Ma, Yuchen Zhang
Predicting drug-related associations is an important task in drug development and discovery. With the rapid advancement of high-throughput technologies and various biological and medical data, artificial intelli-gence (AI), especially progress in machine learning (ML) and deep learning (DL), has paved a new way for the development of drug-related associations prediction. Many studies have been conducted in the literature to predict drug-related associations. This study looks at various computational methods used for drug-related associations prediction with the hope of getting a better insight into the computational methods used. The various computational methods involved in drug-related associations prediction have been re-viewed in this work. We have first summarized the drug, target, and disease-related mainstream public da-tasets. Then, we have discussed existing drug similarity, target similarity, and integrated similarity measurement approaches and grouped them according to their suita-bility. We have then comprehensively investigated drug-related associations and introduced relevant computa-tional methods. Finally, we have briefly discussed the challenges involved in predicting drug-related associa-tions. We discovered that quite a few studies have used implemented ML and DL approaches for drug-related associations prediction. The key challenges were well noted in constructing datasets with reasonable neg-ative samples, extracting rich features, and developing powerful prediction models or ensemble strategies. This review presents useful knowledge and future challenges on the subject matter with the hope of promoting further studies on predicting drug-related as-sociations.
{"title":"A Review of Drug-related Associations Prediction Based on Artificial Intelligence Methods","authors":"Xiu-juan Lei, Mei Ma, Yuchen Zhang","doi":"10.2174/1574893618666230707123817","DOIUrl":"https://doi.org/10.2174/1574893618666230707123817","url":null,"abstract":"\u0000\u0000Predicting drug-related associations is an important task in drug development and discovery. With the rapid advancement of high-throughput technologies and various biological and medical data, artificial intelli-gence (AI), especially progress in machine learning (ML) and deep learning (DL), has paved a new way for the development of drug-related associations prediction. Many studies have been conducted in the literature to predict drug-related associations. This study looks at various computational methods used for drug-related associations prediction with the hope of getting a better insight into the computational methods used.\u0000\u0000\u0000\u0000The various computational methods involved in drug-related associations prediction have been re-viewed in this work. We have first summarized the drug, target, and disease-related mainstream public da-tasets. Then, we have discussed existing drug similarity, target similarity, and integrated similarity measurement approaches and grouped them according to their suita-bility. We have then comprehensively investigated drug-related associations and introduced relevant computa-tional methods. Finally, we have briefly discussed the challenges involved in predicting drug-related associa-tions.\u0000\u0000\u0000\u0000We discovered that quite a few studies have used implemented ML and DL approaches for drug-related associations prediction. The key challenges were well noted in constructing datasets with reasonable neg-ative samples, extracting rich features, and developing powerful prediction models or ensemble strategies.\u0000\u0000\u0000\u0000This review presents useful knowledge and future challenges on the subject matter with the hope of promoting further studies on predicting drug-related as-sociations.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41837458","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 : 2023-07-06DOI: 10.2174/1574893618666230706112826
K. Veer, Sachin Kumar
With the rapid advancement in analyzing high-volume and complex data, machine learning has become one of the most critical and essential tools for classification and prediction. This study reviews machine learning (ML) and deep learning (DL) methods for the classification and prediction of biological signals. The effective utilization of the latest technology in numerous applications, along with various challenges and possible solutions, is the main objective of this present study. A PICO-based systematic review is performed to analyze the applications of ML and DL in different biomedical signals, viz. electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and wrist pulse signal from 2015 to 2022. From this analysis, one can measure machine learning's effectiveness and key characteristics of deep learning. This literature survey finds a clear shift toward deep learning techniques compared to machine learning used in the classification of biomedical signals.
{"title":"Evaluation of Current Trends in Biomedical Applications Using Soft Computing","authors":"K. Veer, Sachin Kumar","doi":"10.2174/1574893618666230706112826","DOIUrl":"https://doi.org/10.2174/1574893618666230706112826","url":null,"abstract":"\u0000\u0000With the rapid advancement in analyzing high-volume and complex data, machine learning has become one of the most critical and essential tools for classification and prediction. This study reviews machine learning (ML) and deep learning (DL) methods for the classification and prediction of biological signals. The effective utilization of the latest technology in numerous applications, along with various challenges and possible solutions, is the main objective of this present study. A PICO-based systematic review is performed to analyze the applications of ML and DL in different biomedical signals, viz. electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and wrist pulse signal from 2015 to 2022. From this analysis, one can measure machine learning's effectiveness and key characteristics of deep learning. This literature survey finds a clear shift toward deep learning techniques compared to machine learning used in the classification of biomedical signals.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49230361","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 : 2023-06-14DOI: 10.2174/1574893618666230614093416
Ying Zhang, Qing Liu, Chun-Yan Cui, Ming-Han Liu, Jian Mou, Si-Jing Liao, Yan Liu, Qun Li, Haihua Yang, Ying-Bo Ren, Yue Huang, Run Li
Numerous clinical data and animal models demonstrate that many patients with neuropathic pain suffer from concomitant depressive symptoms. Massive evidence from biological experiments has verified that the medial prefrontal cortex (mPFC), prefrontal cortex, hippocampus, and other brain regions play an influential role in the co-morbidity of neuropathic pain and depression, but the mechanism by which neuropathic pain induces depression remains unclear. In this study, we mined existing publicly available databases of high-throughput sequencing data intending to identify the differentially expressed genes (DEGs) in the process of neuropathic pain-induced depression. This study provides a rudimentary exploration of the mechanism of neuropathic pain-induced depression and provides credible evidence for its management and precaution.
{"title":"Identification of hub genes in neuropathic pain-induced depression","authors":"Ying Zhang, Qing Liu, Chun-Yan Cui, Ming-Han Liu, Jian Mou, Si-Jing Liao, Yan Liu, Qun Li, Haihua Yang, Ying-Bo Ren, Yue Huang, Run Li","doi":"10.2174/1574893618666230614093416","DOIUrl":"https://doi.org/10.2174/1574893618666230614093416","url":null,"abstract":"\u0000\u0000Numerous clinical data and animal models demonstrate that many patients with neuropathic pain suffer from concomitant depressive symptoms.\u0000\u0000\u0000\u0000Massive evidence from biological experiments has verified that the medial prefrontal cortex (mPFC), prefrontal cortex, hippocampus, and other brain regions play an influential role in the co-morbidity of neuropathic pain and depression, but the mechanism by which neuropathic pain induces depression remains unclear.\u0000\u0000\u0000\u0000In this study, we mined existing publicly available databases of high-throughput sequencing data intending to identify the differentially expressed genes (DEGs) in the process of neuropathic pain-induced depression.\u0000\u0000\u0000\u0000This study provides a rudimentary exploration of the mechanism of neuropathic pain-induced depression and provides credible evidence for its management and precaution.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46750910","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：