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 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
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 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
AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression lev...AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.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
This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expre...This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expression data to find a small subset of the most important genes in a set of thousand or tens of thousands of genes from a relatively small number of experimental runs. The method was previously developed and evaluated on artificially generated data and real data sets. Its evaluations consisted of its ability to rank the genes against known truth in simulated data studies and to identify known important genes in real data studies. The purpose of the work described here is to identify a ranked set of genes in an experimental study and then for a few of the most highly ranked unverified genes, experimentally verify their importance.This method was evaluated using the transcriptional response of Escherichia coli to treatment with four distinct inhibitory compounds： nitric oxide, S-nitrosoglutathione, serine hydroxamate and potassium cyanide. Our analysis identified genes previously recognized in the response to these compounds and also identified new genes.Three of these new genes, ycbR, yJhA and yahN, were found to significantly （p-values〈0.002） affect the sensitivityofE, coli to nitric oxide-mediated growth inhibition. Given that the three genes were not highly ranked in the selected ranked set （RS）, these results support strong sensitivity in the ability of the method to successfully identify genes related to challenge by NO and GSNO. This ability to identify genes related to the response to an inhibitory compound is important for engineering tolerance to inhibitory metabolic products, such as biofuels, and utilization of cheap sugar streams, such as biomass-derived sugars or hydrolysate.展开更多
Gene regulatory networks play an important role the molecular mechanism underlying biological processes. Modeling of these networks is an important challenge to be addressed in the post genomic era. Several methods ha...Gene regulatory networks play an important role the molecular mechanism underlying biological processes. Modeling of these networks is an important challenge to be addressed in the post genomic era. Several methods have been proposed for estimating gene networks from gene expression data. Computational methods for development of network models and analysis of their functionality have proved to be valuable tools in bioinformatics applications. In this paper we tried to review the different methods for reconstructing gene regulatory networks.展开更多
The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilist...The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.展开更多
Colorectal cancers(CRCs) display a wide variety of genomic aberrations that may be either causally linked to their development and progression, or might serve as biomarkers for their presence. Recent advances in rapid...Colorectal cancers(CRCs) display a wide variety of genomic aberrations that may be either causally linked to their development and progression, or might serve as biomarkers for their presence. Recent advances in rapid high-throughput genetic and genomic analysis have helped to identify a plethora of alterations that can potentially serve as new cancer biomarkers, and thus help to improve CRC diagnosis, prognosis, and treatment. Each distinct data type(copy number variations, gene and micro RNAs expression, Cp G island methylation) provides an investigator with a different, partially independent, and complementary view of the entire genome. However, elucidation of gene function will require more information than can be provided by analyzing a single type of data. The integration of knowledge obtained from different sources is becoming increasingly essential for obtaining an interdisciplinary view of large amounts of information, and also for cross-validating experimental results. The integration of numerous types of genetic and genomic data derived from public sources, and via the use of ad-hoc bioinformatics tools and statistical methods facilitates the discovery and validation of novel, informative biomarkers. This combinatory approach will also enable researchers to more accurately and comprehensively understand the associations between different biologic pathways, mechanisms, and phenomena, and gain new insights into the etiology of CRC.展开更多
We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （...We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.展开更多
There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms ...There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms will seriously underestimate the performance of the minority class, leading to inaccurate diagnosis in clinical trails. This paper presents a skewed gene selection algorithm that introduces a weighted metric into the gene selection procedure. The extracted genes are paired as decision rules to distinguish both classes, with these decision rules then integrated into an ensemble learning framework by majority voting to recognize test examples; thus avoiding tedious data normalization and classifier construction. The mining and integrating of a few reliable decision rules gave higher or at least comparable classification performance than many traditional class imbalance learning algorithms on four benchmark imbalanced cancer gene expression datasets.展开更多
The rapid developments of technologies that generate arrays of gene dataenable a global view of the transcription levels of hundreds of thousands of genes simultaneously.The outlier detection problem for gene data has...The rapid developments of technologies that generate arrays of gene dataenable a global view of the transcription levels of hundreds of thousands of genes simultaneously.The outlier detection problem for gene data has its importance but together with the difficulty ofhigh dimensionality. The sparsity of data in high-dimensional space makes each point a relativelygood outlier in the view of traditional distance-based definitions. Thus, finding outliers in highdimensional data is more complex. In this paper, some basic outlier analysis algorithms arediscussed and a new genetic algorithm is presented. This algorithm is to find best dimensionprojections based on a revised cell-based algorithm and to give explanations to solutions. It cansolve the outlier detection problem for gene expression data and for other high dimensional data aswell.展开更多
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.
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.
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.
基金Supported by the National Natural Science Foundation of China(No.81271019No.61463046)Gansu Province Science Foundation for Youths(No.145RJYA282)
文摘AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.
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This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expression data to find a small subset of the most important genes in a set of thousand or tens of thousands of genes from a relatively small number of experimental runs. The method was previously developed and evaluated on artificially generated data and real data sets. Its evaluations consisted of its ability to rank the genes against known truth in simulated data studies and to identify known important genes in real data studies. The purpose of the work described here is to identify a ranked set of genes in an experimental study and then for a few of the most highly ranked unverified genes, experimentally verify their importance.This method was evaluated using the transcriptional response of Escherichia coli to treatment with four distinct inhibitory compounds： nitric oxide, S-nitrosoglutathione, serine hydroxamate and potassium cyanide. Our analysis identified genes previously recognized in the response to these compounds and also identified new genes.Three of these new genes, ycbR, yJhA and yahN, were found to significantly （p-values〈0.002） affect the sensitivityofE, coli to nitric oxide-mediated growth inhibition. Given that the three genes were not highly ranked in the selected ranked set （RS）, these results support strong sensitivity in the ability of the method to successfully identify genes related to challenge by NO and GSNO. This ability to identify genes related to the response to an inhibitory compound is important for engineering tolerance to inhibitory metabolic products, such as biofuels, and utilization of cheap sugar streams, such as biomass-derived sugars or hydrolysate.
文摘Gene regulatory networks play an important role the molecular mechanism underlying biological processes. Modeling of these networks is an important challenge to be addressed in the post genomic era. Several methods have been proposed for estimating gene networks from gene expression data. Computational methods for development of network models and analysis of their functionality have proved to be valuable tools in bioinformatics applications. In this paper we tried to review the different methods for reconstructing gene regulatory networks.
文摘The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.
基金Supported by Associazione Italiana per la Ricerca sul CancroGrants No.10529 and No.12162funds obtained throughan Italian law that allows taxpayers to allocate 0.5%share of theirincome tax contribution to a research institution of their choice
文摘Colorectal cancers(CRCs) display a wide variety of genomic aberrations that may be either causally linked to their development and progression, or might serve as biomarkers for their presence. Recent advances in rapid high-throughput genetic and genomic analysis have helped to identify a plethora of alterations that can potentially serve as new cancer biomarkers, and thus help to improve CRC diagnosis, prognosis, and treatment. Each distinct data type(copy number variations, gene and micro RNAs expression, Cp G island methylation) provides an investigator with a different, partially independent, and complementary view of the entire genome. However, elucidation of gene function will require more information than can be provided by analyzing a single type of data. The integration of knowledge obtained from different sources is becoming increasingly essential for obtaining an interdisciplinary view of large amounts of information, and also for cross-validating experimental results. The integration of numerous types of genetic and genomic data derived from public sources, and via the use of ad-hoc bioinformatics tools and statistical methods facilitates the discovery and validation of novel, informative biomarkers. This combinatory approach will also enable researchers to more accurately and comprehensively understand the associations between different biologic pathways, mechanisms, and phenomena, and gain new insights into the etiology of CRC.
基金the National Natural Sci-ence Foundation of China (No. 30700161)the Na-tional High-Tech Research and Development Program(863 Program) of China (No. 2007AA01Z167 and2006AA02Z309)+1 种基金China Postdoctoral Science Foun-dation (No. 20070410223)Doctor Scientific Re-search Startup Foundation of Qufu Normal University(No. Bsqd2007036).
文摘We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.
基金Supported by the National Natural Science Foundation of China (No.61105057)the Ph.D Foundation of Jiangsu University of Science and Technology (Nos.35301002 and 35211104)
文摘There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms will seriously underestimate the performance of the minority class, leading to inaccurate diagnosis in clinical trails. This paper presents a skewed gene selection algorithm that introduces a weighted metric into the gene selection procedure. The extracted genes are paired as decision rules to distinguish both classes, with these decision rules then integrated into an ensemble learning framework by majority voting to recognize test examples; thus avoiding tedious data normalization and classifier construction. The mining and integrating of a few reliable decision rules gave higher or at least comparable classification performance than many traditional class imbalance learning algorithms on four benchmark imbalanced cancer gene expression datasets.
文摘The rapid developments of technologies that generate arrays of gene dataenable a global view of the transcription levels of hundreds of thousands of genes simultaneously.The outlier detection problem for gene data has its importance but together with the difficulty ofhigh dimensionality. The sparsity of data in high-dimensional space makes each point a relativelygood outlier in the view of traditional distance-based definitions. Thus, finding outliers in highdimensional data is more complex. In this paper, some basic outlier analysis algorithms arediscussed and a new genetic algorithm is presented. This algorithm is to find best dimensionprojections based on a revised cell-based algorithm and to give explanations to solutions. It cansolve the outlier detection problem for gene expression data and for other high dimensional data aswell.
