Objective:To use the gene chip of pseudomonas aeruginosa as a research sample and to explore it at an omics level,aiming at elucidating the co-expression network characteristics of the virulence genes exoS and exoU of...Objective:To use the gene chip of pseudomonas aeruginosa as a research sample and to explore it at an omics level,aiming at elucidating the co-expression network characteristics of the virulence genes exoS and exoU of pseudomonas aeruginosa in the lower respiratory tract from the perspective of molecular biology and identifying its key regulatory genes.Methods:From March 2016 to May 2018,312 patients infected with pseudomonas aeruginosa in the lower respiratory tract who were admitted to Department of Respiratory Medicine of Baogang Hospital and given follow-up treatments in the hospital were selected as subjects by use of cluster sampling.Alveolar lavage fluid and sputum collected from those patients were used as biological specimens.The genes of pseudomonas aeruginosa were detected with the help of oligonucleotide probes to make a pre-processing of chip data.A total of 8 common antibiotics(ceftazidime,gentamicin,piperacillin,amikacin,ciprofloxacin,levofloxacin,doripenem and ticarcillin)against Gram-negative bacteria were selected to determine the drug resistance of biological specimens.MCODE algorithm was used to construct a co-expression network model of the drug-resistance genes focused on exoS/exoU.Results:The expression level of exoS/exoU in the drug-resistance group was significantly higher than that in the non-resistance group(p<0.05).The top 5 differentially expressed genes in the alveolar lavage fluid specimens from the drug-resistance group were RAC1,ITGB1,ITGB5,CRK and IGF1R in the order from high to low.In the sputum specimens,the top 5 differentially expressed genes were RAC1,CRK,IGF1R,ITGB1 and ITGB5.In the alveolar lavage fluid specimens,only RAC1 had a positive correlation with the expression of exoS and exoU(p<0.05).In the sputum specimens,RAC1,ITGB1,ITGB5,CRK and IGF1R were positively correlated with the expression of exoS and exoU(p<0.05).The genes included in the co-expression network contained exoS,exoU,RAC1,ITGB1,ITGB5,CRK,CAMK2D,RHOA,FLNA,IGF1R,TGFBR2 and FOS.Among them,RAC1 had a highest score in the aspect of regulatory ability(72.00)and the largest number of regulatory genes(6);followed by ITGB1,ITGB5 and CRK genes.Conclusions:The high expression of exoS and exoU in the sputum specimens suggests that pseudomonas aeruginosa has a higher probability to get resistant to antibiotics;RAC1,ITGB1,ITGB5 and CRK genes may be the key genes that can regulate the expression of exoS and exoU.展开更多
DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expres...DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.展开更多
CHDTEPDB(URL:http://chdtepdb.com/)is a manually integrated database for congenital heart disease(CHD)that stores the expression profiling data of CHD derived from published papers,aiming to provide rich resources for i...CHDTEPDB(URL:http://chdtepdb.com/)is a manually integrated database for congenital heart disease(CHD)that stores the expression profiling data of CHD derived from published papers,aiming to provide rich resources for investigating a deeper correlation between human CHD and aberrant transcriptome expression.The develop-ment of human diseases involves important regulatory roles of RNAs,and expression profiling data can reflect the underlying etiology of inherited diseases.Hence,collecting and compiling expression profiling data is of critical significance for a comprehensive understanding of the mechanisms and functions that underpin genetic diseases.CHDTEPDB stores the expression profiles of over 200 sets of 7 types of CHD and provides users with more convenient basic analytical functions.Due to the differences in clinical indicators such as disease type and unavoidable detection errors among various datasets,users are able to customize their selection of corresponding data for personalized analysis.Moreover,we provide a submission page for researchers to submit their own data so that increasing expression profiles as well as some other histological data could be supplemented to the database.CHDTEPDB is a user-friendly interface that allows users to quickly browse,retrieve,download,and analyze their target samples.CHDTEPDB will significantly improve the current knowledge of expression profiling data in CHD and has the potential to be exploited as an important tool for future research on the disease.展开更多
Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. Micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode p...Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. Micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messengerRNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with thevolume of data containing imprecision and outliers increases the challenges indealing with them. Clustering methods are hence essential to identify the patternspresent in massive gene data. Many techniques involve hierarchical, partitioning,grid based, density based, model based and soft clustering approaches for dealingwith the gene expression data. Understanding the gene regulation and other usefulinformation from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate onproviding a soft clustering approach for analyzing the gene expression data. Thepopulation elements are grouped based on the fuzziness principle and a degree ofmembership is assigned to all the elements. An improved Fuzzy clustering byLocal Approximation of Memberships (FLAME) is proposed in this workwhich overcomes the limitations of the other approaches while dealing with thenon-linear relationships and provide better segregation of biological functions.展开更多
In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary cha...In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.展开更多
Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists...Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.展开更多
Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based...Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based models for classifying cancer types using machine learning techniques. By applying Log2 normalization to gene expression data and conducting Wilcoxon rank sum tests, the researchers employed various classifiers and Incremental Feature Selection (IFS) strategies. The study culminated in two optimized models using the XGBoost classifier, comprising 10 and 74 genes respectively. The 10-gene model, due to its simplicity, is proposed for easier clinical implementation, whereas the 74-gene model exhibited superior performance in terms of Specificity, AUC (Area Under the Curve), and Precision. These models were evaluated based on their sensitivity, AUC, and specificity, aiming to achieve high sensitivity and AUC while maintaining reasonable specificity.展开更多
In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity...In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity measure. On the basis of the similarity measures of protein- protein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Per-formance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning coefficients of the combined dissimilarity measure. The results show that it is very helpful and meaningful to incorporate het-erogeneous data sources in clustering gene expression data, and those coefficients for the genome-wide or completed data sources should be given larger values when constructing the combined dissimilarity measure.展开更多
The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pr...The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.展开更多
文摘Objective:To use the gene chip of pseudomonas aeruginosa as a research sample and to explore it at an omics level,aiming at elucidating the co-expression network characteristics of the virulence genes exoS and exoU of pseudomonas aeruginosa in the lower respiratory tract from the perspective of molecular biology and identifying its key regulatory genes.Methods:From March 2016 to May 2018,312 patients infected with pseudomonas aeruginosa in the lower respiratory tract who were admitted to Department of Respiratory Medicine of Baogang Hospital and given follow-up treatments in the hospital were selected as subjects by use of cluster sampling.Alveolar lavage fluid and sputum collected from those patients were used as biological specimens.The genes of pseudomonas aeruginosa were detected with the help of oligonucleotide probes to make a pre-processing of chip data.A total of 8 common antibiotics(ceftazidime,gentamicin,piperacillin,amikacin,ciprofloxacin,levofloxacin,doripenem and ticarcillin)against Gram-negative bacteria were selected to determine the drug resistance of biological specimens.MCODE algorithm was used to construct a co-expression network model of the drug-resistance genes focused on exoS/exoU.Results:The expression level of exoS/exoU in the drug-resistance group was significantly higher than that in the non-resistance group(p<0.05).The top 5 differentially expressed genes in the alveolar lavage fluid specimens from the drug-resistance group were RAC1,ITGB1,ITGB5,CRK and IGF1R in the order from high to low.In the sputum specimens,the top 5 differentially expressed genes were RAC1,CRK,IGF1R,ITGB1 and ITGB5.In the alveolar lavage fluid specimens,only RAC1 had a positive correlation with the expression of exoS and exoU(p<0.05).In the sputum specimens,RAC1,ITGB1,ITGB5,CRK and IGF1R were positively correlated with the expression of exoS and exoU(p<0.05).The genes included in the co-expression network contained exoS,exoU,RAC1,ITGB1,ITGB5,CRK,CAMK2D,RHOA,FLNA,IGF1R,TGFBR2 and FOS.Among them,RAC1 had a highest score in the aspect of regulatory ability(72.00)and the largest number of regulatory genes(6);followed by ITGB1,ITGB5 and CRK genes.Conclusions:The high expression of exoS and exoU in the sputum specimens suggests that pseudomonas aeruginosa has a higher probability to get resistant to antibiotics;RAC1,ITGB1,ITGB5 and CRK genes may be the key genes that can regulate the expression of exoS and exoU.
文摘DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
文摘CHDTEPDB(URL:http://chdtepdb.com/)is a manually integrated database for congenital heart disease(CHD)that stores the expression profiling data of CHD derived from published papers,aiming to provide rich resources for investigating a deeper correlation between human CHD and aberrant transcriptome expression.The develop-ment of human diseases involves important regulatory roles of RNAs,and expression profiling data can reflect the underlying etiology of inherited diseases.Hence,collecting and compiling expression profiling data is of critical significance for a comprehensive understanding of the mechanisms and functions that underpin genetic diseases.CHDTEPDB stores the expression profiles of over 200 sets of 7 types of CHD and provides users with more convenient basic analytical functions.Due to the differences in clinical indicators such as disease type and unavoidable detection errors among various datasets,users are able to customize their selection of corresponding data for personalized analysis.Moreover,we provide a submission page for researchers to submit their own data so that increasing expression profiles as well as some other histological data could be supplemented to the database.CHDTEPDB is a user-friendly interface that allows users to quickly browse,retrieve,download,and analyze their target samples.CHDTEPDB will significantly improve the current knowledge of expression profiling data in CHD and has the potential to be exploited as an important tool for future research on the disease.
文摘Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. Micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messengerRNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with thevolume of data containing imprecision and outliers increases the challenges indealing with them. Clustering methods are hence essential to identify the patternspresent in massive gene data. Many techniques involve hierarchical, partitioning,grid based, density based, model based and soft clustering approaches for dealingwith the gene expression data. Understanding the gene regulation and other usefulinformation from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate onproviding a soft clustering approach for analyzing the gene expression data. Thepopulation elements are grouped based on the fuzziness principle and a degree ofmembership is assigned to all the elements. An improved Fuzzy clustering byLocal Approximation of Memberships (FLAME) is proposed in this workwhich overcomes the limitations of the other approaches while dealing with thenon-linear relationships and provide better segregation of biological functions.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/42/43)This work was supported by Taif University Researchers Supporting Program(project number:TURSP-2020/200),Taif University,Saudi Arabia.
文摘In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.
文摘Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.
文摘Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based models for classifying cancer types using machine learning techniques. By applying Log2 normalization to gene expression data and conducting Wilcoxon rank sum tests, the researchers employed various classifiers and Incremental Feature Selection (IFS) strategies. The study culminated in two optimized models using the XGBoost classifier, comprising 10 and 74 genes respectively. The 10-gene model, due to its simplicity, is proposed for easier clinical implementation, whereas the 74-gene model exhibited superior performance in terms of Specificity, AUC (Area Under the Curve), and Precision. These models were evaluated based on their sensitivity, AUC, and specificity, aiming to achieve high sensitivity and AUC while maintaining reasonable specificity.
文摘In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity measure. On the basis of the similarity measures of protein- protein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Per-formance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning coefficients of the combined dissimilarity measure. The results show that it is very helpful and meaningful to incorporate het-erogeneous data sources in clustering gene expression data, and those coefficients for the genome-wide or completed data sources should be given larger values when constructing the combined dissimilarity measure.
文摘The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.
