Displaying publications 1 - 20 of 283 in total

  1. Toh C, Mohd-Hairul AR, Ain NM, Namasivayam P, Go R, Abdullah NAP, et al.
    BMC Res Notes, 2017 Nov 02;10(1):554.
    PMID: 29096695 DOI: 10.1186/s13104-017-2872-6
    BACKGROUND: Vanda Mimi Palmer (VMP) is commercially valuable for its strong fragrance but little is known regarding the fragrance production and emission sites on the flowers.

    RESULTS: Olfactory perception detected fragrance only from the petals and sepals. Light and Environmental Scanning Electron microscopy analyses on fresh tissues showed distributions of stomata and trichomes concentrated mostly around the edges. These results paralleled the rich starch deposits and intense neutral red stain, indicating strong fragrance and trichomes as potential main fragrance release sites. Next Generation Sequencing (NGS) transcriptomic data of adaxial and abaxial layers of the tissues showed monoterpene synthase transcripts specifically linalool and ocimene synthases distributed throughout the tissues. qPCR analyses taken at different time points revealed high levels of linalool and ocimene synthases transcripts in the early morning with maximal level at 4.00 am but remained low throughout daylight hours.

    CONCLUSIONS: Knowledge of the VMP floral anatomy and its fragrance production characteristics, which complemented our previous molecular and biochemical data on VMP, provided additional knowledge on how fragrance and flower morphology are closely intertwined. Further investigation on the mechanisms of fragrance biosynthesis and interaction of potential pollinators would elucidate the evolution of the flower morphology to maximize the reproduction success of this plant.

    Matched MeSH terms: Gene Expression Profiling/methods*
  2. Aizat WM, Ismail I, Noor NM
    Adv. Exp. Med. Biol., 2018 11 2;1102:1-9.
    PMID: 30382565 DOI: 10.1007/978-3-319-98758-3_1
    The central dogma of molecular biology (DNA, RNA, protein and metabolite) has engraved our understanding of genetics in all living organisms. While the concept has been embraced for many decades, the development of high-throughput technologies particularly omics (genomics, transcriptomics, proteomics and metabolomics) has revolutionised the field to incorporate big data analysis including bioinformatics and systems biology as well as synthetic biology area. These omics approaches as well as systems and synthetic biology areas are now increasingly popular as seen by the growing numbers of publication throughout the years. Several journals which have published most of these related fields are also listed in this chapter to overview their impact and target journals.
    Matched MeSH terms: Gene Expression Profiling/trends*
  3. Lau NS, Foong CP, Kurihara Y, Sudesh K, Matsui M
    PLoS ONE, 2014;9(1):e86368.
    PMID: 24466058 DOI: 10.1371/journal.pone.0086368
    The photosynthetic cyanobacterium, Synechocystis sp. strain 6803, is a potential platform for the production of various chemicals and biofuels. In this study, direct photosynthetic production of a biopolymer, polyhydroxyalkanoate (PHA), in genetically engineered Synechocystis sp. achieved as high as 14 wt%. This is the highest production reported in Synechocystis sp. under photoautotrophic cultivation conditions without the addition of a carbon source. The addition of acetate increased PHA accumulation to 41 wt%, and this value is comparable to the highest production obtained with cyanobacteria. Transcriptome analysis by RNA-seq coupled with real-time PCR was performed to understand the global changes in transcript levels of cells subjected to conditions suitable for photoautotrophic PHA biosynthesis. There was lower expression of most PHA synthesis-related genes in recombinant Synechocystis sp. with higher PHA accumulation suggesting that the concentration of these enzymes is not the limiting factor to achieving high PHA accumulation. In order to cope with the higher PHA production, cells may utilize enhanced photosynthesis to drive the product formation. Results from this study suggest that the total flux of carbon is the possible driving force for the biosynthesis of PHA and the polymerizing enzyme, PHA synthase, is not the only critical factor affecting PHA-synthesis. Knowledge of the regulation or control points of the biopolymer production pathways will facilitate the further use of cyanobacteria for biotechnological applications.
    Matched MeSH terms: Gene Expression Profiling*
  4. Ng KH, Ho CK, Phon-Amnuaisuk S
    PLoS ONE, 2012;7(10):e47216.
    PMID: 23071763 DOI: 10.1371/journal.pone.0047216
    Clustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact that these clustering methods work satisfactorily on a majority of the EST datasets, they have a common weakness. They are prone to deliver unsatisfactory clustering results when dealing with ESTs from the genes derived from the same family. The root cause is the distance measures applied on them are not sensitive enough to separate these closely related genes.
    Matched MeSH terms: Gene Expression Profiling/methods*
  5. Saghir FS, Rose IM, Dali AZ, Shamsuddin Z, Jamal AR, Mokhtar NM
    Int. J. Gynecol. Cancer, 2010 Jul;20(5):724-31.
    PMID: 20973258
    INTRODUCTION: Malignant transformation of type I endometrium involves alteration in gene expression with subsequent uncontrolled proliferation of altered cells.

    OBJECTIVE: The main objective of the present study was to identify the cancer-related genes and gene pathways in the endometrium of healthy and cancer patients.

    MATERIALS AND METHODS: Thirty endometrial tissues from healthy and type I EC patients were subjected to total RNA isolation. The RNA samples with good integrity number were hybridized to a new version of Affymetrix Human Genome GeneChip 1.0 ST array. We analyzed the results using the GeneSpring 9.0 GX and the Pathway Studio 6.1 software. For validation assay, quantitative real-time polymerase chain reaction was used to analyze 4 selected genes in normal and EC tissue.

    RESULTS: Of the 28,869 genes profiled, we identified 621 differentially expressed genes (2-fold) in the normal tissue and the tumor. Among these genes, 146 were up-regulated and 476 were down-regulated in the tumor as compared with the normal tissue (P < 0.001). Up-regulated genes included the v-erb-a erythroblastic leukemia viral oncogene homolog 3 (ErbB3), ErbB4, E74-like factor 3 (ELF3), and chemokine ligand 17 (CXCL17). The down-regulated genes included signal transducer and activator transcription 5B (STAT5b), transforming growth factor A receptor III (TGFA3), caveolin 1 (CAV1), and protein kinase C alpha (PKCA). The gene set enrichment analysis showed 10 significant gene sets with related genes (P < 0.05). The quantitative polymerase chain reaction of 4 selected genes using similar RNA confirmed the microarray results (P < 0.05).

    CONCLUSIONS: Identification of molecular pathways with their genes related to type I EC contribute to the understanding of pathophysiology of this cancer, probably leading to identifying potential biomarkers of the cancer.

    Matched MeSH terms: Gene Expression Profiling*
  6. Azlina A, Samsudin AR
    Med. J. Malaysia, 2004 May;59 Suppl B:166-7.
    PMID: 15468870
    In Malaysia, the field of genomics in toxicology is still in infancy. The purpose of this study is to focus on the use of toxicogenomics for determination of gene expressions changes in cultured human fibroblast cells treated with genotoxicology free biomaterial (using Ames test), a locally produced hyroxyapatite. Dose and time response is similar to Ames test with time interval up to 21 days. mRNA is extracted, followed with RT-PCR and polyacrilamide gel electrophoresis. Changes of the gene expressions compared to the non-treated fibroblast mRNA would suggest some gene interactions in the molecule level associated with the exposure of the fibroblast cell line to the biomaterials. Further analysis (cloning & sequencing) shall be carried out to investigate the genes involved as simple changes might not signified toxicity.
    Matched MeSH terms: Gene Expression Profiling*
  7. Reza Etemadi M, Ling KH, Zainal Abidin S, Chee HY, Sekawi Z
    PLoS ONE, 2017;12(5):e0176947.
    PMID: 28558071 DOI: 10.1371/journal.pone.0176947
    Human rhinovirus (HRV) is the common virus that causes acute respiratory infection (ARI) and is frequently associated with lower respiratory tract infections (LRTIs). We aimed to investigate whether HRV infection induces a specific gene expression pattern in airway epithelial cells. Alveolar epithelial cell monolayers were infected with HRV species B (HRV-B). RNA was extracted from both supernatants and infected monolayer cells at 6, 12, 24 and 48 hours post infection (hpi) and transcriptional profile was analyzed using Affymetrix GeneChip and the results were subsequently validated using quantitative Real-time PCR method. HRV-B infects alveolar epithelial cells which supports implication of the virus with LRTIs. In total 991 genes were found differentially expressed during the course of infection. Of these, 459 genes were up-regulated whereas 532 genes were down-regulated. Differential gene expression at 6 hpi (187 genes up-regulated vs. 156 down-regulated) were significantly represented by gene ontologies related to the chemokines and inflammatory molecules indicating characteristic of viral infection. The 75 up-regulated genes surpassed the down-regulated genes (35) at 12 hpi and their enriched ontologies fell into discrete functional entities such as regulation of apoptosis, anti-apoptosis, and wound healing. At later time points of 24 and 48 hpi, predominated down-regulated genes were enriched for extracellular matrix proteins and airway remodeling events. Our data provides a comprehensive image of host response to HRV infection. The study suggests the underlying molecular regulatory networks genes which might be involved in pathogenicity of the HRV-B and potential targets for further validations and development of effective treatment.
    Matched MeSH terms: Gene Expression Profiling*
  8. Moorthy K, Jaber AN, Ismail MA, Ernawan F, Mohamad MS, Deris S
    Methods Mol. Biol., 2019;1986:255-266.
    PMID: 31115893 DOI: 10.1007/978-1-4939-9442-7_12
    In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371-385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data.
    Matched MeSH terms: Gene Expression Profiling*
  9. Bhalla R, Narasimhan K, Swarup S
    Plant Cell Rep., 2005 Dec;24(10):562-71.
    PMID: 16220342
    A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics.
    Matched MeSH terms: Gene Expression Profiling/methods; Gene Expression Profiling/trends*
  10. Sahebi M, Hanafi MM, Azizi P, Hakim A, Ashkani S, Abiri R
    Mol. Biotechnol., 2015 Oct;57(10):880-903.
    PMID: 26271955 DOI: 10.1007/s12033-015-9884-z
    Suppression subtractive hybridization (SSH) is an effective method to identify different genes with different expression levels involved in a variety of biological processes. This method has often been used to study molecular mechanisms of plants in complex relationships with different pathogens and a variety of biotic stresses. Compared to other techniques used in gene expression profiling, SSH needs relatively smaller amounts of the initial materials, with lower costs, and fewer false positives present within the results. Extraction of total RNA from plant species rich in phenolic compounds, carbohydrates, and polysaccharides that easily bind to nucleic acids through cellular mechanisms is difficult and needs to be considered. Remarkable advancement has been achieved in the next-generation sequencing (NGS) field. As a result of progress within fields related to molecular chemistry and biology as well as specialized engineering, parallelization in the sequencing reaction has exceptionally enhanced the overall read number of generated sequences per run. Currently available sequencing platforms support an earlier unparalleled view directly into complex mixes associated with RNA in addition to DNA samples. NGS technology has demonstrated the ability to sequence DNA with remarkable swiftness, therefore allowing previously unthinkable scientific accomplishments along with novel biological purposes. However, the massive amounts of data generated by NGS impose a substantial challenge with regard to data safe-keeping and analysis. This review examines some simple but vital points involved in preparing the initial material for SSH and introduces this method as well as its associated applications to detect different novel genes from different plant species. This review evaluates general concepts, basic applications, plus the probable results of NGS technology in genomics, with unique mention of feasible potential tools as well as bioinformatics.
    Matched MeSH terms: Gene Expression Profiling/economics; Gene Expression Profiling/methods
  11. Selvarajah GT, Bonestroo FAS, Timmermans Sprang EPM, Kirpensteijn J, Mol JA
    BMC Vet. Res., 2017 Nov 25;13(1):354.
    PMID: 29178874 DOI: 10.1186/s12917-017-1281-3
    BACKGROUND: Quantitative PCR (qPCR) is a common method for quantifying mRNA expression. Given the heterogeneity present in tumor tissues, it is crucial to normalize target mRNA expression data using appropriate reference genes that are stably expressed under a variety of pathological and experimental conditions. No studies have validated specific reference genes in canine osteosarcoma (OS). Previous gene expression studies involving canine OS have used one or two reference genes to normalize gene expression. This study aimed to validate a panel of reference genes commonly used for normalization of canine OS gene expression data using the geNorm algorithm. qPCR analysis of nine canine reference genes was performed on 40 snap-frozen primary OS tumors and seven cell lines.

    RESULTS: Tumors with a variety of clinical and pathological characteristics were selected. Gene expression stability and the optimal number of reference genes for gene expression normalization were calculated. RPS5 and HNRNPH were highly stable among OS cell lines, while RPS5 and RPS19 were the best combination for primary tumors. Pairwise variation analysis recommended four and two reference genes for optimal normalization of the expression data of canine OS tumors and cell lines, respectively.

    CONCLUSIONS: Appropriate combinations of reference genes are recommended to normalize mRNA levels in canine OS tumors and cell lines to facilitate standardized and reliable quantification of target gene expression, which is essential for investigating key genes involved in canine OS metastasis and for comparative biomarker discovery.

    Matched MeSH terms: Gene Expression Profiling/methods; Gene Expression Profiling/veterinary*
  12. Wong MY, Govender NT, Ong CS
    BMC Res Notes, 2019 Sep 24;12(1):631.
    PMID: 31551084 DOI: 10.1186/s13104-019-4652-y
    OBJECTIVE: Basal stem rot disease causes severe economic losses to oil palm production in South-east Asia and little is known on the pathogenicity of the pathogen, the basidiomyceteous Ganoderma boninense. Our data presented here aims to identify both the house-keeping and pathogenicity genes of G. boninense using Illumina sequencing reads.

    DESCRIPTION: The hemibiotroph G. boninense establishes via root contact during early stage of colonization and subsequently kills the host tissue as the disease progresses. Information on the pathogenicity factors/genes that causes BSR remain poorly understood. In addition, the molecular expressions corresponding to G. boninense growth and pathogenicity are not reported. Here, six transcriptome datasets of G. boninense from two contrasting conditions (three biological replicates per condition) are presented. The first datasets, collected from a 7-day-old axenic condition provide an insight onto genes responsible for sustenance, growth and development of G. boninense while datasets of the infecting G. boninense collected from oil palm-G. boninense pathosystem (in planta condition) at 1 month post-inoculation offer a comprehensive avenue to understand G. boninense pathogenesis and infection especially in regard to molecular mechanisms and pathways. Raw sequences deposited in Sequence Read Archive (SRA) are available at NCBI SRA portal with PRJNA514399, bioproject ID.

    Matched MeSH terms: Gene Expression Profiling/methods*; Gene Expression Profiling/statistics & numerical data
  13. Wasito I, Hashim SZ, Sukmaningrum S
    Bioinformation, 2007 Dec 30;2(5):175-81.
    PMID: 18305825
    Gene expression profiling plays an important role in the identification of biological and clinical properties of human solid tumors such as colorectal carcinoma. Profiling is required to reveal underlying molecular features for diagnostic and therapeutic purposes. A non-parametric density-estimation-based approach called iterative local Gaussian clustering (ILGC), was used to identify clusters of expressed genes. We used experimental data from a previous study by Muro and others consisting of 1,536 genes in 100 colorectal cancer and 11 normal tissues. In this dataset, the ILGC finds three clusters, two large and one small gene clusters, similar to their results which used Gaussian mixture clustering. The correlation of each cluster of genes and clinical properties of malignancy of human colorectal cancer was analysed for the existence of tumor or normal, the existence of distant metastasis and the existence of lymph node metastasis.
    Matched MeSH terms: Gene Expression Profiling
  14. Han Z, Sun J, Lv A, Xian JA, Sung YY, Sun X, et al.
    Fish Shellfish Immunol., 2018 Sep;80:291-301.
    PMID: 29886138 DOI: 10.1016/j.fsi.2018.06.007
    To better understand gene expression in the intestine after Shewanella algae infection and provide insights into its immune roles in the tongue sole, Cynoglossus semilaevis, sequencing-based high-throughput RNA analysis (RNA-Seq) for the intestines between the control group and 12 h post-injection group was performed. After assembly, there was an average of 23,957,159 raw sequencing reads, and 23,943,491 clean reads were obtained after filtering out low-quality reads. Then, 383 differentially expressed genes (DEGs) in the intestines in response to S. algae infection were identified. Subsequently, gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs were conducted to further explore their functions. Among all of the pathways involved, sixteen pathways were related to the immune system, among which the complement and coagulation cascades pathway was the most prominent for immunity-related DEGs, followed by the leukocyte transendothelial migration pathway. Furthermore, the expression levels of twelve selected DEGs in the immune-related pathways were identified by quantitative real-time polymerase chain reaction, substantiating the reliability and reproducibility of the RNA-Seq results. In summary, this study represents an important genomic resource for understanding the potential immune role of the tongue sole intestine from the perspective of gene expression.
    Matched MeSH terms: Gene Expression Profiling
  15. Suppiah, J., Sakinah, S., Chan, S.Y., Wong, Y.P., Subbiah, S.K., Chee, H.Y., et al.
    Human platelets are anucleate cells that lack in deoxyribonucleic acid (DNA), thus hampering genomic study on them. However, the presence of their own messenger ribonucleic acid (mRNA) transcript allows functional study via the transcriptome approach. Transcriptome not only allows profiling of platelet but also aids in studying gene regulation in virus infections and other diseases that have an impact on platelets. Some viruses are known to affect the platelet either by causing a reduction or destruction. Dengue virus is one of the most postulated virus having such effect and frequently linked to platelet reduction. The transcriptome approach has a pivotal role in providing a deeper insight to link certain diseases and their effect on platelets. This review critically discusses role of platelet in dengue and other viral diseases of public health relevance, with a specific focus on the methods currently used in platelet transcriptome profiling.
    Matched MeSH terms: Gene Expression Profiling
  16. Lee XW, Mat-Isa MN, Mohd-Elias NA, Aizat-Juhari MA, Goh HH, Dear PH, et al.
    PLoS ONE, 2016;11(12):e0167958.
    PMID: 27977777 DOI: 10.1371/journal.pone.0167958
    Rafflesia is a biologically enigmatic species that is very rare in occurrence and possesses an extraordinary morphology. This parasitic plant produces a gigantic flower up to one metre in diameter with no leaves, stem or roots. However, little is known about the floral biology of this species especially at the molecular level. In an effort to address this issue, we have generated and characterised the transcriptome of the Rafflesia cantleyi flower, and performed a comparison with the transcriptome of its floral bud to predict genes that are expressed and regulated during flower development. Approximately 40 million sequencing reads were generated and assembled de novo into 18,053 transcripts with an average length of 641 bp. Of these, more than 79% of the transcripts had significant matches to annotated sequences in the public protein database. A total of 11,756 and 7,891 transcripts were assigned to Gene Ontology categories and clusters of orthologous groups respectively. In addition, 6,019 transcripts could be mapped to 129 pathways in Kyoto Encyclopaedia of Genes and Genomes Pathway database. Digital abundance analysis identified 52 transcripts with very high expression in the flower transcriptome of R. cantleyi. Subsequently, analysis of differential expression between developing flower and the floral bud revealed a set of 105 transcripts with potential role in flower development. Our work presents a deep transcriptome resource analysis for the developing flower of R. cantleyi. Genes potentially involved in the growth and development of the R. cantleyi flower were identified and provide insights into biological processes that occur during flower development.
    Matched MeSH terms: Gene Expression Profiling
  17. Chong PP, Chin VK, Wong WF, Madhavan P, Yong VC, Looi CY
    Genes (Basel), 2018 Nov 07;9(11).
    PMID: 30405082 DOI: 10.3390/genes9110540
    Candida albicans is an opportunistic fungal pathogen, which causes a plethora of superficial, as well as invasive, infections in humans. The ability of this fungus in switching from commensalism to active infection is attributed to its many virulence traits. Biofilm formation is a key process, which allows the fungus to adhere to and proliferate on medically implanted devices as well as host tissue and cause serious life-threatening infections. Biofilms are complex communities of filamentous and yeast cells surrounded by an extracellular matrix that confers an enhanced degree of resistance to antifungal drugs. Moreover, the extensive plasticity of the C. albicans genome has given this versatile fungus the added advantage of microevolution and adaptation to thrive within the unique environmental niches within the host. To combat these challenges in dealing with C. albicans infections, it is imperative that we target specifically the molecular pathways involved in biofilm formation as well as drug resistance. With the advent of the -omics era and whole genome sequencing platforms, novel pathways and genes involved in the pathogenesis of the fungus have been unraveled. Researchers have used a myriad of strategies including transcriptome analysis for C. albicans cells grown in different environments, whole genome sequencing of different strains, functional genomics approaches to identify critical regulatory genes, as well as comparative genomics analysis between C. albicans and its closely related, much less virulent relative, C. dubliniensis, in the quest to increase our understanding of the mechanisms underlying the success of C. albicans as a major fungal pathogen. This review attempts to summarize the most recent advancements in the field of biofilm and antifungal resistance research and offers suggestions for future directions in therapeutics development.
    Matched MeSH terms: Gene Expression Profiling
  18. Raabe CA, Tang TH, Brosius J, Rozhdestvensky TS
    Nucleic Acids Res., 2014 Feb;42(3):1414-26.
    PMID: 24198247 DOI: 10.1093/nar/gkt1021
    High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, we review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.
    Matched MeSH terms: Gene Expression Profiling/methods*
  19. Kasim S, Deris S, Othman RM
    Comput. Biol. Med., 2013 Sep;43(9):1120-33.
    PMID: 23930805 DOI: 10.1016/j.compbiomed.2013.05.011
    A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the "multi-stage filtering-Clustering Functional Annotation" (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.
    Matched MeSH terms: Gene Expression Profiling/methods*
  20. Moriya S, Ogawa S, Parhar IS
    Biochem. Biophys. Res. Commun., 2013 Jun 14;435(4):562-6.
    PMID: 23669040 DOI: 10.1016/j.bbrc.2013.05.004
    Most vertebrates possess at least two gonadotropin-releasing hormone (GnRH) neuron types. To understand the physiological significance of the multiple GnRH systems in the brain, we examined three GnRH neuron type-specific transcriptomes using single-cell microarray analyses in the medaka (Oryzias latipes). A microarray profile of the three GnRH neuron types revealed five genes that are uniquely expressed in specific GnRH neuron types. GnRH1 neurons expressed three genes that are homologous to functionally characterised genes, GnRH2 neurons uniquely expressed one unnamed gene, and GnRH3 neurons uniquely expressed one known gene. These genes may be involved in the modulation or maintenance of each GnRH neuron type.
    Matched MeSH terms: Gene Expression Profiling/methods*
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