Difference analysis of myeloid leukemia fusion oncogene expression network based on time series / 中华疾病控制杂志

@#Objective Focusing on four types acute myeloid leukemia ( AML) fusion oncogenes，so as to explore the network difference with time series expression data and further identify important genes in networks． Methods Gene network difference analysis was conducted while focusing on the global attributes of the union network． The CompNet neighborhood similarity index ( CNSI) was adopted to assess network similarity．“fast-greedy”algorithm was used to detect communities based on the union network，and further identify hub genes． Ｒesults The CNSI value between NUP98-HOXA9-3 d and NUP98-HOXA9-8 d was 0． 73，while AML1-ETO-6 h and PML-ＲAＲA-6 h was 0．25． We identified ten AML associated genes and sev- en of them ( TNF，VEGFA，EP300，EGF，CD44，PTGS2，SMAD3) were reported in the literature． Conclu- sions The network difference analysis revealed the pattern and heterogeneity of AML gene expression change across different time points，and further provided target genes for efficient treatment of AML with different types of fusion oncogenes．

COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification

Biclustering is an increasingly used data mining technique for searching groups of co-expressed genes across the subset ofexperimental conditions from the gene-expression data. The group of co-expressed genes is present in the form of variouspatterns called a bicluster. A bicluster provides significant insights related to the functionality of genes and plays animportant role in various clinical applications such as drug discovery, biomarker discovery, gene network analysis, geneidentification, disease diagnosis, pathway analysis etc. This paper presents a novel unsupervised approach ‘COmprehensiveSearch for Column-Coherent Evolution Biclusters (COSCEB)’ for a comprehensive search of biologically significantcolumn-coherent evolution biclusters. The concept of column subspace extraction from each gene pair and LongestCommon Contiguous Subsequence (LCCS) is employed to identify significant biclusters. The experiments have beenperformed on both synthetic as well as real datasets. The performance of COSCEB is evaluated with the help of key issues.The issues are comprehensive search, Deep OPSM bicluster, bicluster types, bicluster accuracy, bicluster size, noise,overlapping, output nature, computational complexity and biologically significant biclusters. The performance of COSCEBis compared with six all-time famous biclustering algorithms SAMBA, OPSM, xMotif, Bimax, Deep OPSM- and UniBic.The result shows that the proposed approach performs effectively on most of the issues and extracts all possible biologicallysignificant column-coherent evolution biclusters which are far more than other biclustering algorithms. Along with theproposed approach, we have also presented the case study which shows the application of significant biclusters for hub geneidentification.

Data mining and influential analysis of gene expression data for plant resistance gene identification in tomato (Solanum lycopersicum)

Background Molecular mechanisms of plant-pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.

Standard-based Integration of Heterogeneous Large-scale DNA Microarray Data for Improving Reusability

Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/

Recent advances in gene expression data clustering: a case study with comparative results

Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.