Pub Date : 2023-08-16DOI: 10.2174/1574893618666230816141723
Xiaoqing Peng, Wanxin Cui, Wenjin Zhang, Zihao Li, Xiaoshu Zhu, L. Yuan, Ji Li
Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison. In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval. By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer. Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).
{"title":"A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data","authors":"Xiaoqing Peng, Wanxin Cui, Wenjin Zhang, Zihao Li, Xiaoshu Zhu, L. Yuan, Ji Li","doi":"10.2174/1574893618666230816141723","DOIUrl":"https://doi.org/10.2174/1574893618666230816141723","url":null,"abstract":"\u0000\u0000Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison.\u0000\u0000\u0000\u0000In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval.\u0000\u0000\u0000\u0000By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer.\u0000\u0000\u0000\u0000Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).\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":"42313996","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 : 2023-08-16DOI: 10.2174/1574893618666230816090548
Yaoyao Lu, Junkai Liu, T. Jiang, Zhiming Cui, Hongjie Wu
New drugs are costly, time-consuming, and often accompanied by safety concerns. With the development of deep learning, computer-aided drug design has become more mainstream, and convolutional neural networks and graph neural networks have been widely used for drug–target affinity (DTA) prediction. The paper proposes a method of predicting DTA using graph convolutional networks and multiscale convolutional neural networks. We construct drug molecules into graph representation vectors and learn feature expressions through graph attention networks and graph convolutional networks. A three-branch convolutional neural network learns the local and global features of protein sequences, and the two feature representations are merged into a regression module to predict the DTA. We present a novel model to predict DTA, with a 2.5% improvement in the consistency index and a 21% accuracy improvement in terms of the mean squared error on the Davis dataset compared to DeepDTA. Morever, our method outperformed other mainstream DTA prediction models namely, GANsDTA, WideDTA, GraphDTA and DeepAffinity. The results showed that the use of multiscale convolutional neural networks was better than a single-branched convolutional neural network at capturing protein signatures and the use of graphs to express drug molecules yielded better results.
{"title":"Drug–target binding affinity prediction based on three-branched multiscale convolutional neural networks","authors":"Yaoyao Lu, Junkai Liu, T. Jiang, Zhiming Cui, Hongjie Wu","doi":"10.2174/1574893618666230816090548","DOIUrl":"https://doi.org/10.2174/1574893618666230816090548","url":null,"abstract":"\u0000\u0000New drugs are costly, time-consuming, and often accompanied by safety concerns. With the development of deep learning, computer-aided drug design has become more mainstream, and convolutional neural networks and graph neural networks have been widely used for drug–target affinity (DTA) prediction.\u0000\u0000\u0000\u0000The paper proposes a method of predicting DTA using graph convolutional networks and multiscale convolutional neural networks.\u0000\u0000\u0000\u0000We construct drug molecules into graph representation vectors and learn feature expressions through graph attention networks and graph convolutional networks. A three-branch convolutional neural network learns the local and global features of protein sequences, and the two feature representations are merged into a regression module to predict the DTA.\u0000\u0000\u0000\u0000We present a novel model to predict DTA, with a 2.5% improvement in the consistency index and a 21% accuracy improvement in terms of the mean squared error on the Davis dataset compared to DeepDTA. Morever, our method outperformed other mainstream DTA prediction models namely, GANsDTA, WideDTA, GraphDTA and DeepAffinity.\u0000\u0000\u0000\u0000The results showed that the use of multiscale convolutional neural networks was better than a single-branched convolutional neural network at capturing protein signatures and the use of graphs to express drug molecules yielded better results.\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":"44958222","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 : 2023-08-15DOI: 10.2174/1574893618666230815121150
Leying Pan, T. Zhang, Qiang Yang, Guoping Yang, Nan Han, Shaojie Qiao
Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast tumors and medical imaging has begun to use deep-learning-based approaches. In this study, the TumorDet model is proposed to detect the benign and malignant lesions of breast tumor, which has positive significance for assisting doctors in diagnosis. We use the proposed TumorDet to analyze and predict breast tumors on the real MRI dataset. (1) We introduce an adaptive gamma correction (AGC) method to balance brightness equalization and increase the contrast of mammography images; (2) we use the ShuffleNet model to exchange information between different feature layers and extract the hidden high-level features of medical images; and (3) we use the transfer learning method to fine-tune the ShuffleNet model and obtain the optimal parameters. The proposed TumorDet model has shown that accuracy, sensitivity, and specificity reach 90.43%, 89.37%, and 87.81%, respectively. TumorDet performs well in the breast tumor detection task. In addition, we use the proposed TumorDet to conduct experiments on other tasks, such as forest fires, and the robustness of TumorDet is proved by experimental results. TumorDet employs the ShuffleNet model to exchange information between different feature layers without increasing the number of network parameters and applies transfer learning methods to further extract the basic features of medical images by fine-tuning. The model is beneficial for the localization and classification of breast tumors and also performs well in forest fire detection.
{"title":"TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet","authors":"Leying Pan, T. Zhang, Qiang Yang, Guoping Yang, Nan Han, Shaojie Qiao","doi":"10.2174/1574893618666230815121150","DOIUrl":"https://doi.org/10.2174/1574893618666230815121150","url":null,"abstract":"\u0000\u0000Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast tumors and medical imaging has begun to use deep-learning-based approaches. In this study, the TumorDet model is proposed to detect the benign and malignant lesions of breast tumor, which has positive significance for assisting doctors in diagnosis.\u0000\u0000\u0000\u0000We use the proposed TumorDet to analyze and predict breast tumors on the real MRI dataset.\u0000\u0000\u0000\u0000(1) We introduce an adaptive gamma correction (AGC) method to balance brightness equalization and increase the contrast of mammography images; (2) we use the ShuffleNet model to exchange information between different feature layers and extract the hidden high-level features of medical images; and (3) we use the transfer learning method to fine-tune the ShuffleNet model and obtain the optimal parameters.\u0000\u0000\u0000\u0000The proposed TumorDet model has shown that accuracy, sensitivity, and specificity reach 90.43%, 89.37%, and 87.81%, respectively. TumorDet performs well in the breast tumor detection task. In addition, we use the proposed TumorDet to conduct experiments on other tasks, such as forest fires, and the robustness of TumorDet is proved by experimental results.\u0000\u0000\u0000\u0000TumorDet employs the ShuffleNet model to exchange information between different feature layers without increasing the number of network parameters and applies transfer learning methods to further extract the basic features of medical images by fine-tuning. The model is beneficial for the localization and classification of breast tumors and also performs well in forest fire detection.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49632050","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 : 2023-08-07DOI: 10.2174/1574893618666230807123016
Baoping Zhu, Huizhen Geng, Fan Yang, Yanxin Wu, Tiefeng Cao, Dongyu Wang, Zilian Wang
Preeclampsia (PE) is a severe pregnancy complication associated with autophagy. This research sought to uncover autophagy-related genes in pre-eclampsia through bioinformatics and machine learning. GSE75010 from the GEO series was subjected to WGCNA to identify key modular genes in PE. Autophagy genes retrieved from the THANATOS overlapped with the modular genes to yield PE-related autophagy genes. Furthermore, the crucial step involved the utilization of two machine learning algorithms (LASSO and SVM-RFE) for dimensionality reduction. The candidate gene was further verified by quantitative reverse transcription polymerase chain reaction, western blot, and immunohistochemistry. Preliminary experiments were conducted on HTR-8/SVneo cell lines to explore the role of candidate genes in autophagy regulation. WGCNA identified 291 genes from 5 hubs, and after overlapping with 1087 autophagy-related genes obtained from THANATOS, 42 PE-related ARGs were identified. ANXA6 was recognized as a potential target through SVM-RFE and LASSO analyses. The mRNA and protein expression of ANXA6 were verified in placenta samples. In HTR8/SVneo cells, modulating ANXA6 expression altered autophagy levels. Knocking down ANXA6 resulted in an anti-autophagy effect, which was reversed by treatment with CAL101, an inhibitor of PI3K, Akt, and mTOR. We observed that ANXA6 may serve as a possible PE action target and that autophagy may be crucial to the pathogenesis of PE.
{"title":"Revealing ANXA6 as a Novel Autophagy-related Target for Pre-eclampsia Based on the Machine Learning","authors":"Baoping Zhu, Huizhen Geng, Fan Yang, Yanxin Wu, Tiefeng Cao, Dongyu Wang, Zilian Wang","doi":"10.2174/1574893618666230807123016","DOIUrl":"https://doi.org/10.2174/1574893618666230807123016","url":null,"abstract":"\u0000\u0000Preeclampsia (PE) is a severe pregnancy complication associated with autophagy.\u0000\u0000\u0000\u0000This research sought to uncover autophagy-related genes in pre-eclampsia through bioinformatics and machine learning.\u0000\u0000\u0000\u0000GSE75010 from the GEO series was subjected to WGCNA to identify key modular genes in PE. Autophagy genes retrieved from the THANATOS overlapped with the modular genes to yield PE-related autophagy genes. Furthermore, the crucial step involved the utilization of two machine learning algorithms (LASSO and SVM-RFE) for dimensionality reduction. The candidate gene was further verified by quantitative reverse transcription polymerase chain reaction, western blot, and immunohistochemistry. Preliminary experiments were conducted on HTR-8/SVneo cell lines to explore the role of candidate genes in autophagy regulation.\u0000\u0000\u0000\u0000WGCNA identified 291 genes from 5 hubs, and after overlapping with 1087 autophagy-related genes obtained from THANATOS, 42 PE-related ARGs were identified. ANXA6 was recognized as a potential target through SVM-RFE and LASSO analyses. The mRNA and protein expression of ANXA6 were verified in placenta samples. In HTR8/SVneo cells, modulating ANXA6 expression altered autophagy levels. Knocking down ANXA6 resulted in an anti-autophagy effect, which was reversed by treatment with CAL101, an inhibitor of PI3K, Akt, and mTOR.\u0000\u0000\u0000\u0000We observed that ANXA6 may serve as a possible PE action target and that autophagy may be crucial to the pathogenesis of PE.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45513523","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}
RNA editing enriches post-transcriptional sequence changes. Currently detecting RNA editing sites is mostly based on the Sanger sequencing platform and second-generation sequencing. However, detection with Sanger sequencing is limited by the disturbing background peaks using the direct sequencing method and the clone number using the clone sequencing method, while second-generation sequencing detection is constrained by its short read. We aimed to design a pipeline that can accurately detect RNA editing sites for full-length long-read amplicons to meet the requirement when focusing on a few specific genes of interest. We developed a novel high-throughput RNA editing sites detection pipeline based on the PacBio circular consensus sequences sequencing which is accurate with high-throughput and long-read coverage. We tested the pipeline on cytosolic malate dehydrogenase in the hard-shelled mussel Mytilus coruscus and further validated it using direct Sanger sequencing. Data generated from the PacBio circular consensus sequences (CCS) amplicons in three mussels were first filtered by quality and then selected by open reading frame. After filtering, 225-2047 sequences of the three mussels, respectively, were used to identify RNA editing sites. With corresponding genomic DNA sequences, we extracted 227-799 candidate RNA editing sites excluding heterozygous sites. We further figured out 7-11 final RESs using a new error model specially designed for RNA editing site detection. The resulting RNA editing sites all agree with the validation using the Sanger sequencing. We report a near-zero error rate method in identifying RNA editing sites of long-read amplicons with the use of PacBio CCS sequencing.
{"title":"Full-length PacBio Amplicon Sequencing to Unveil RNA Editing Sites","authors":"Xiao-lu Zhu, Ming-ling Liao, Ya-Jie Zhu, Yun‐wei Dong","doi":"10.2174/1574893618666230803112142","DOIUrl":"https://doi.org/10.2174/1574893618666230803112142","url":null,"abstract":"\u0000\u0000RNA editing enriches post-transcriptional sequence changes. Currently detecting RNA editing sites is mostly based on the Sanger sequencing platform and second-generation sequencing. However, detection with Sanger sequencing is limited by the disturbing background peaks using the direct sequencing method and the clone number using the clone sequencing method, while second-generation sequencing detection is constrained by its short read.\u0000\u0000\u0000\u0000We aimed to design a pipeline that can accurately detect RNA editing sites for full-length long-read amplicons to meet the requirement when focusing on a few specific genes of interest.\u0000\u0000\u0000\u0000We developed a novel high-throughput RNA editing sites detection pipeline based on the PacBio circular consensus sequences sequencing which is accurate with high-throughput and long-read coverage. We tested the pipeline on cytosolic malate dehydrogenase in the hard-shelled mussel Mytilus coruscus and further validated it using direct Sanger sequencing.\u0000\u0000\u0000\u0000Data generated from the PacBio circular consensus sequences (CCS) amplicons in three mussels were first filtered by quality and then selected by open reading frame. After filtering, 225-2047 sequences of the three mussels, respectively, were used to identify RNA editing sites. With corresponding genomic DNA sequences, we extracted 227-799 candidate RNA editing sites excluding heterozygous sites. We further figured out 7-11 final RESs using a new error model specially designed for RNA editing site detection. The resulting RNA editing sites all agree with the validation using the Sanger sequencing.\u0000\u0000\u0000\u0000We report a near-zero error rate method in identifying RNA editing sites of long-read amplicons with the use of PacBio CCS sequencing.\u0000","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45187580","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 : 2023-07-31DOI: 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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