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Journal articles on the topic 'Gene expression analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

R, Dr Prema. "Feature Selection for Gene Expression Data Analysis – A Review." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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2

Liu, Junjie, Peng Li, Liuyang Lu, Lanfen Xie, Xiling Chen, and Baizhong Zhang. "Selection and evaluation of potential reference genes for gene expression analysis in Avena fatua Linn." Plant Protection Science 55, No. 1 (2018): 61–71. http://dx.doi.org/10.17221/20/2018-pps.

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Eight commonly used candidate reference genes, 18S ribosomal RNA (rRNA) (18S), 28S rRNA (28S), actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), elongation factor 1 alpha (EF1α), ribosomal protein L7 (RPL7), Alpha-tubulin (α-TUB), and TATA box binding protein-associated factor (TBP), were evaluated under various experimental conditions to assess their suitability in different developmental stages, tissues and herbicide treatments in Avena fatua. The results indicated the most suitable reference genes for the different experimental conditions. For developmental stages, 28S and EF1α

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3

Anitha, S., and Dr C. P. Chandran. "Review on Analysis of Gene Expression Data Using Biclustering Approaches." Bonfring International Journal of Data Mining 6, no. 2 (2016): 16–23. http://dx.doi.org/10.9756/bijdm.8135.

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4

S, Kavya. "A Review on: Gene Expression Analysis Techniques and its Application." International Journal of Research Publication and Reviews 5, no. 4 (2024): 9928–33. http://dx.doi.org/10.55248/gengpi.5.0424.1145.

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5

YASUE, Hiroshi, Koji DOI, and Hideki HIRAIWA. "Gene Expression Analysis." Journal of Animal Genetics 48, no. 1 (2019): 9–18. http://dx.doi.org/10.5924/abgri.48.9.

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6

Oetting, William S. "Gene Expression Analysis." Pigment Cell Research 13, no. 1 (2000): 21–27. http://dx.doi.org/10.1034/j.1600-0749.2000.130105.x.

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7

Carvalho, Felicia I., Christopher Johns, and Marc E. Gillespie. "Gene expression analysis." Biochemistry and Molecular Biology Education 40, no. 3 (2012): 181–90. http://dx.doi.org/10.1002/bmb.20588.

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8

Winter, Holger, Kerstin Korn, and Rudolf Rigler. "Direct Gene Expression Analysis." Current Pharmaceutical Biotechnology 5, no. 2 (2004): 191–97. http://dx.doi.org/10.2174/1389201043376995.

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9

Stein,, Richard A. "Gene-Expression Analysis Redefined." Genetic Engineering & Biotechnology News 31, no. 7 (2011): 1–31. http://dx.doi.org/10.1089/gen.31.7.13.

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10

Kozian, D. "Comparative gene-expression analysis." Trends in Biotechnology 17, no. 2 (1999): 73–78. http://dx.doi.org/10.1016/s0167-7799(98)01292-x.

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11

Brazma, Alvis, and Jaak Vilo. "Gene expression data analysis." FEBS Letters 480, no. 1 (2000): 17–24. http://dx.doi.org/10.1016/s0014-5793(00)01772-5.

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12

Kriete, Andres. "Gene expression analysis enriched." Drug Discovery Today 9, no. 21 (2004): 913–14. http://dx.doi.org/10.1016/s1359-6446(04)03255-6.

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13

Balaji, P., and A. P Siva Kumar. "A Link-Based Cluster Ensemble Approach for Improved Gene Expression Data Analysis." International Journal of Scientific Engineering and Research 2, no. 12 (2014): 14–19. https://doi.org/10.70729/j2013416.

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14

Mikami, Koji. "Requirement for Different Normalization Genes for Quantitative Gene Expression Analysis Under Abiotic Stress Conditions in ‘Bangia’ sp. ESS1." Journal of Aquatic Research and Marine Sciences 02, no. 03 (2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.202037.

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15

Mikami, Koji. "Requirement for Different Normalization Genes for Quantitative Gene Expression Analysis Under Abiotic Stress Conditions in ‘Bangia’ sp. ESS1." Journal of Aquatic Research and Marine Sciences 02, no. 03 (2019): 194–205. http://dx.doi.org/10.29199/2639-4618/arms.203037.

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16

Tanabata, Takanari, Fumiaki Hirose, Hidenobu Hashikami, and Hajime Nobuhara. "Interactive Data Mining Tool for Microarray Data Analysis Using Formal Concept Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 2 (2012): 273–81. http://dx.doi.org/10.20965/jaciii.2012.p0273.

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The DNA microarray analysis can explain gene functions by measuring tens of thousands of gene expressions at once and analyzing gene expression profiles that are obtained from the measurement. However, gene expression profiles have such a vast amount of information and therefore most analyses work are done on the data narrowed down by statistical methods, there remains a possibility ofmissing out on genes that consist the factors of phenomena from their evaluations. This study propose a method based on a formal concept analysis to visualize all gene expression profiles and characteristic infor

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17

Halpert,, Richard L. "Improving Gene-Expression Data Analysis." Genetic Engineering & Biotechnology News 32, no. 5 (2012): 38–39. http://dx.doi.org/10.1089/gen.32.5.16.

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18

Daniels, David. "Gene Expression Analysis Making Inroads." Genetic Engineering & Biotechnology News 33, no. 6 (2013): 20, 22–23. http://dx.doi.org/10.1089/gen.33.6.10.

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19

Ivanov, Hristo Y., Vili K. Stoyanova, Nikolay T. Popov, M. Bosheva, and Tihomir I. Vachev. "Blood-Based Gene Expression in children with Autism spectrum disorder." BioDiscovery 17 (September 30, 2015): e8966. https://doi.org/10.7750/BioDiscovery.2015.17.2.

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Comparative gene expression profiling analysis is useful in discovering differentially expressed genes associated with various diseases, including mental disorders. Autism spectrum disorder (ASD) is a severe neuropsychiatric disorder which has complex pathobiology with profound influences of genetic factors in its development. Although numerous autism susceptible genes were identified, the etiology of autism is not fully explained. The study aimed to examine gene expression profiling in peripheral blood from 60 individuals divided into two groups: children with ASD and age- and gender-matched

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20

Burian, Dennis. "Exon-Level Gene Expression Analysis." Aviation, Space, and Environmental Medicine 80, no. 6 (2009): 577–78. http://dx.doi.org/10.3357/asem.21004.2009.

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21

Velculescu, Victor E., and Kenneth W. Kinzler. "Gene expression analysis goes digital." Nature Biotechnology 25, no. 8 (2007): 878–80. http://dx.doi.org/10.1038/nbt0807-878.

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22

Curtis, R. Keira, and Martin D. Brand. "Control analysis of gene expression." Biochemical Society Transactions 30, no. 1 (2002): A8. http://dx.doi.org/10.1042/bst030a008.

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23

Curtis, R. Keira, and Martin D. Brand. "Control analysis of gene expression." Biochemical Society Transactions 30, no. 1 (2002): A32. http://dx.doi.org/10.1042/bst030a032.

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24

Yoshida, Tetsuo, Takehisa Suzuki, Hironori Sato, Hiroshi Nishina, and Hideo Iba. "Analysis offra-2 gene expression." Nucleic Acids Research 21, no. 11 (1993): 2715–21. http://dx.doi.org/10.1093/nar/21.11.2715.

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25

Velculescu, V. E., L. Zhang, B. Vogelstein, and K. W. Kinzler. "Serial Analysis of Gene Expression." Science 270, no. 5235 (1995): 484–87. http://dx.doi.org/10.1126/science.270.5235.484.

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26

Sharma, Gunjan, Ansh Malik, Satyendra Tripathi, Vishwajit Deshmukh, and Ashlesh Patil. "Gene expression analysis of Schizophrenia." Bioinformation 20, no. 11 (2024): 1441–46. https://doi.org/10.6026/9732063002001441.

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Schizophrenia is a chronic psychiatric disorder marked by cognitive deficits associated with prefrontal cortical dysfunction, particularly in Broadmann Area 10 (BA 10), where gray matter reduction is observed. The genetic mechanisms behind these abnormalities remain unclear. Therefore, it is of interest to analyze altered gene expression and pathways in the prefrontal cortex of schizophrenia patients. We used two GEO datasets - GSE12654 (discovery) and GSE17612 (validation) and differential gene expression was assessed between schizophrenia patients and healthy controls. Validation confirmed t

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27

Dunn, C. W., X. Luo, and Z. Wu. "Phylogenetic Analysis of Gene Expression." Integrative and Comparative Biology 53, no. 5 (2013): 847–56. http://dx.doi.org/10.1093/icb/ict068.

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28

Patino, Willmar D., Omar Y. Mian, and Paul M. Hwang. "Serial Analysis of Gene Expression." Circulation Research 91, no. 7 (2002): 565–69. http://dx.doi.org/10.1161/01.res.0000036018.76903.18.

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29

Carson, Monica J., J. Cameron Thrash, and David Lo. "Analysis of Microglial Gene Expression." American Journal of PharmacoGenomics 4, no. 5 (2004): 321–30. http://dx.doi.org/10.2165/00129785-200404050-00005.

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30

Hu, Min, and Kornelia Polyak. "Serial analysis of gene expression." Nature Protocols 1, no. 4 (2006): 1743–60. http://dx.doi.org/10.1038/nprot.2006.269.

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31

Lovén, Jakob, David A. Orlando, Alla A. Sigova, et al. "Revisiting Global Gene Expression Analysis." Cell 151, no. 3 (2012): 476–82. http://dx.doi.org/10.1016/j.cell.2012.10.012.

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32

Zhao, Weiguo, Rongfang Li, Dandan Chen, et al. "Cloning and expression pattern analysis of MmPOD12 gene in mulberry under abiotic stresses." Journal of Experimental Biology and Agricultural Sciences 4, VIS (2017): 698–705. http://dx.doi.org/10.18006/2016.4(vis).698.705.

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33

Shi, T., Y. Xu, M. J. Yang, et al. "Genetic variation, association analysis, and expression pattern of SMAD3 gene in Chinese cattle." Czech Journal of Animal Science 61, No. 5 (2016): 209–16. http://dx.doi.org/10.17221/34/2015-cjas.

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34

Tejashwini. N, Tejashwini N., Tanushree Chaudhuri, and Kusum Paul. "Regulation of Nuclear Gene Expression Data Analysis in Diabetic Nephropathy and Data Mining." International Journal of Scientific Research 2, no. 8 (2012): 48–50. http://dx.doi.org/10.15373/22778179/aug2013/17.

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35

Bandapalli, Lekhya. "Spatial Analysis of Gene Expression: Impact of Long-Distance Running on Cellular Pathways." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 1459–73. http://dx.doi.org/10.21275/sr24216221746.

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36

Lykhenko, O. "СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA". Biotechnologia Acta 14, № 1 (2021): 38–45. http://dx.doi.org/10.15407/biotech14.01.38.

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The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The sou

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37

Lykhenko, O. "СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA". Biotechnologia Acta 14, № 1 (2021): 38–45. https://doi.org/10.15407/biotech14.01.038.

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The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The sou

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38

Bao, W. B., L. Ye, Z. Y. Pan, et al. "Analysis of polymorphism in the porcine TLR4 gene and its expression related to Escherichia coliF18 infection." Czech Journal of Animal Science 56, No. 11 (2011): 475–82. http://dx.doi.org/10.17221/3836-cjas.

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The genetic variation in exon 1 of the TLR4 gene was detected among a total of 893 animals, including Asian wild boars, 3 imported commercial and 10 Chinese indigenous pig breeds. The expression of TLR4 was assayed by RT-PCR and different expression between resistant and sensitive resource populations to ETEC F18 was analysed to discuss the role that the TLR4 gene plays in resistance. In this study, new alleles were detected in exon 1 of the TLR4 gene. These polymorphisms are significantly different between Chinese indigenous breeds and imported breeds. Based on the published TLR4 gene sequenc

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39

Ma, Fang, Yali Zou, Ruilin Ma, Xin Chen, and Lanfang Ma. "Evolution, characterization and expression analysis of Sox gene family in rainbow trout (Oncorhynchus mykiss)." Czech Journal of Animal Science 67, No. 4 (2022): 157–66. http://dx.doi.org/10.17221/4/2022-cjas.

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The Sox transcription factor family plays an important role in various biological processes such as animal sex determination and multiple organ development. We used online databases to analyze the gene structure, chemical characteristics, and evolutionary relationship of Sox family genes through bioinformatics, and we studied the expression profiles and regulatory mechanisms of Sox family genes. A total of 29 rainbow trout Sox genes were identified. The phylogenetic analysis found that Sox genes of rainbow trout were clustered in seven subfamilies (B1, B2, C, D, E, F and H), and the gene struc

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40

Yan, Shankai, and Ka-Chun Wong. "GESgnExt: Gene Expression Signature Extraction and Meta-Analysis on Gene Expression Omnibus." IEEE Journal of Biomedical and Health Informatics 24, no. 1 (2020): 311–18. http://dx.doi.org/10.1109/jbhi.2019.2896144.

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41

Ye, Shui Qing, David C. Usher, and Li Q. Zhang. "Gene Expression Profiling of Human Diseases by Serial Analysis of Gene Expression." Journal of Biomedical Science 9, no. 5 (2002): 384–94. http://dx.doi.org/10.1159/000064547.

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42

Qing Ye, Shui, David C. Usher, and Li Q. Zhang. "Gene expression profiling of human diseases by serial analysis of gene expression." Journal of Biomedical Science 9, no. 5 (2002): 384–94. http://dx.doi.org/10.1007/bf02256531.

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43

Pandurangan, Sujatha, T.Geetharathan, and G.Madhusudhan. "Weighted Gene Co-expression Network Analysis of Glioblastoma Gene Expression Microarray Data." Journal of Advanced Zoology 44, no. 4 (2023): 105–24. http://dx.doi.org/10.17762/jaz.v44i4.1270.

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Glioblastoma is a highly aggressive and lethal form of brain cancer characterized by its complex molecular landscape. Understanding the underlying gene expression patterns and their relationships is essential for unraveling the mechanisms driving this disease. In this study, we conducted a Weighted Gene Co-expression Network Analysis (WGCNA) on Glioblastoma gene expression microarray data to identify co-expressed gene modules and potential key regulatory genes associated with the disease. Utilizing a comprehensive dataset of Glioblastoma samples, we performed quality control and preprocessing

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44

Park, Young-Kyu, Jeffrey L. Franklin, Stephen H. Settle, et al. "Gene expression profile analysis of mouse colon embryonic development." genesis 41, no. 1 (2005): 1–12. http://dx.doi.org/10.1002/gene.20088.

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45

Anusha.B.N, Anusha B. N., Shambu M. G. Shambu.M.G, and Kusum Paul. "Genome Wide Transcriptional Analysis of Gene Expression Signatures and Pathways on Neoplastic Pancreatic Cancer." International Journal of Scientific Research 2, no. 8 (2012): 43–44. http://dx.doi.org/10.15373/22778179/aug2013/15.

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46

Stein, Richard A. "Gene Expression Analysis Reshapes Biomedical Research." Genetic Engineering & Biotechnology News 32, no. 17 (2012): 34–39. http://dx.doi.org/10.1089/gen.32.17.15.

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47

Weldon, Don, and Grace Johnston. "Gene Expression Analysis in Living Cells." Genetic Engineering & Biotechnology News 33, no. 9 (2013): 20–21. http://dx.doi.org/10.1089/gen.33.9.10.

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48

BROWN, VANESSA M., ALEX OSSADTCHI, ARSHAD H. KHAN, et al. "Gene expression tomography." Physiological Genomics 8, no. 2 (2002): 159–67. http://dx.doi.org/10.1152/physiolgenomics.00090.2001.

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Gene expression tomography, or GET, is a new method to increase the speed of three-dimensional (3-D) gene expression analysis in the brain. The name is evocative of the method’s dual foundations in high-throughput gene expression analysis and computerized tomographic image reconstruction, familiar from techniques such as positron emission tomography (PET) and X-ray computerized tomography (CT). In GET, brain slices are taken using a cryostat in conjunction with axial rotation about independent axes to create a series of “views” of the brain. Gene expression information obtained from the axiall

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49

BAKIR, Melike, and Cebrail YILDIRIM. "Isolation and expression analysis of ascorbate peroxidase (APX) gene in lentil (Lens culinaris Medik.) under drought stress conditions." Ege Üniversitesi Ziraat Fakültesi Dergisi 59, no. 3 (2022): 439–47. http://dx.doi.org/10.20289/zfdergi.1007041.

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Objective: The objective of this study was to isolate partial cDNA that belongs to the ascorbate peroxidase (APX) gene of lentil (Lens culinaris Medik.) and to express LcAPX gene in lentil seedlings under drought stress conditions. Material and Methods: To identify the relationships between drought stress and LcAPX gene expression, lentil seedlings grown for 2 weeks were subjected to drought stress through not irrigating for 6, 13, and 20 days. Effects of drought stress were determined by measuring the stem relative water content (RWC). Gene expression changes in lentil seedlings were determin

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50

Nishida, N., K. Kurata, and A. Suyama. "Gene expression analysis by DNA computing." Seibutsu Butsuri 40, supplement (2000): S152. http://dx.doi.org/10.2142/biophys.40.s152_4.

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