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Journal articles on the topic 'Gene expression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ashwin, S. S., and Masaki Sasai. "2P132 Dynamics of transcriptional apparatus in eukaryotic gene expression(08. Molecular genetics & Gene expression,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S180. http://dx.doi.org/10.2142/biophys.53.s180_6.

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2

Takaiwa, F., T. Yoshihara, H. Washida, et al. "Characterization of common cis-regulatory elements responsible for endosperm-specific expression of rice glutelin gene." International Rice Research Notes 21, no. 2-3 (1996): 17. https://doi.org/10.5281/zenodo.7247011.

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This article 'Characterization of common cis-regulatory elements responsible for endosperm-specific expression of rice glutelin gene' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain det

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3

Nesvadbová, M., and A. Knoll. "Evaluation of reference genes for gene expression studies in pig muscle tissue by real-time PCR." Czech Journal of Animal Science 56, No. 5 (2011): 213–16. http://dx.doi.org/10.17221/1428-cjas.

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The selection of reference genes is essential for gene expression studies when using a real-time quantitative polymerase chain reaction (PCR). Reference gene selection should be performed for each experiment because the gene expression level may be changed in different experimental conditions. In this study, the stability of mRNA expression was determined for seven genes: HPRT1, RPS18, NACA, TBP, TAF4B, RPL32 and OAZ1. The stability of these reference genes was investigated in the skeletal muscle tissue of pig foetuses, piglets and adult pigs using real-time quantitative PCR and SYBR green che

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4

Muthukalathi, Selvamani, Ravanan Ramanujam, and Anbupalam Thalamuthu. "Consensus Clustering for Microarray Gene Expression Data." Bonfring International Journal of Data Mining 4, no. 4 (2014): 26–33. http://dx.doi.org/10.9756/bijdm.6140.

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5

Prima, V. I. "Proposals for the ISS: «Expression» Experiment Gene expression in plants in microgravity." Kosmìčna nauka ì tehnologìâ 6, no. 4 (2000): 100. http://dx.doi.org/10.15407/knit2000.04.101.

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6

Mirzaei, Alireza, Sara Dezhsetan, Gholamreza Kavoosi, and Mahdi Behnamian. "The Effects of Silveroxide on mRNA Level of Key Genes Involved In the Biosynthesis of Rebaudioside-A in Stevia Rebaudiana." Journal of Cell and Molecular Research 9, no. 2 (2022): 67–72. https://doi.org/10.22067/jcmr.v9i2.69816.

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Stevia rebaudiana bertoni is a plant with sweetening properties. This medicinal plant is perennial and from Asteraceae family. Its leaves contain glycoside compounds of a sugar part and non-sugar sectors. One of the glycosides compounds is RebaudiosideA which has a greater importance in market. Several key regulating genes including copalyl diphosphate synthase (CPPS) (AF034545.1), geranylgeranyl diphosphate synthase (GGDPS) (DQ432013.3), (E)-4-hydroxy-3- methylbut-2-enyl diphosphate synthase (HDS) (FJ755689.1), UDP glucosyltranserase 85C2(UGT85C2)(AY345978.1), UDPglucosyltranserase–74G1

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7

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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8

Li, M., X. Wu, X. Guo, et al. "Identification of optimal reference genes for examination of gene expression in different tissues of fetal yaks." Czech Journal of Animal Science 62, No. 10 (2017): 426–34. http://dx.doi.org/10.17221/75/2016-cjas.

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Reverse transcription quantitative real-time PCR (RT-qPCR) is widely used to study the relative abundance of mRNA transcripts because of its sensitivity and reliable quantification. However, the reliability of the interpretation of expression data is influenced by several complex factors, including RNA quality, transcription activity, and PCR efficiency, among others. To avoid experimental errors arising from potential variation, the selection of appropriate reference genes to normalize gene expression is essential. In this study, 10 commonly used reference genes – ACTB, B2M, HPRT1, GAPDH, 18S

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9

Vijayachandra, K., K. Palanichelvam, and K. Veluthambi. "Induction of Agrobacterium tumefaciens vir genes by rice." International Rice Research Notes 21, no. 2-3 (1996): 36–37. https://doi.org/10.5281/zenodo.7247093.

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This article 'Induction of Agrobacterium tumefaciens vir genes by rice' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research reported.

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10

Kemp, Martin. "Gene expression." Nature 446, no. 7135 (2007): 496. http://dx.doi.org/10.1038/446496a.

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11

Lamond, Angus I. "Gene expression." Trends in Biochemical Sciences 18, no. 4 (1993): 149. http://dx.doi.org/10.1016/0968-0004(93)90027-k.

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12

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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13

Antonín, Stratil, Horák Pavel, Nesvadbová Michaela, et al. "Genomic structure and expression of the porcine ACTC1 gene." Czech Journal of Animal Science 63, No. 9 (2018): 371–78. http://dx.doi.org/10.17221/34/2018-cjas.

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A partial cDNA (~1200 bp) of the porcine ACTC1 gene was identified in the subtracted foetal hind limb muscle cDNA library (44 days of gestation; using m. biceps femoris cDNA as the driver). Using specific polymerase chain reaction (PCR) primers, a bacterial artificial chromosome (BAC) clone containing the genomic ACTC1 gene was identified and the gene was sequenced. Specific PCR primers designed from the BAC and cDNA sequences were used for amplification and comparative sequencing of ACTC1 of Pietrain and Meishan pigs. The gene is approximately 5.4 kb in length, is composed of 7 exons, and has

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14

M, Aminafshar, Bahrampour V, Bagizadeh A, Emam Jomeh Kashan N, and Mohamad Abadi M.R. "Expression of CD44 Gene in Goat’s Oocytes and Embryos." Greener Journal of Biological Sciences 4, no. 5 (2014): 139–45. http://dx.doi.org/10.15580/gjbs.2014.5.050614223.

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15

Venmadhi, S., and K. K. Narayanan. "Mitochondrial gene expression in cytoplasmic male sterile and fertile lines of rice." International Rice Research Notes 23, no. 1 (1998): 10. https://doi.org/10.5281/zenodo.6947619.

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This article 'Mitochondrial gene expression in cytoplasmic male sterile and fertile lines of rice' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research reported.

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16

Luka, Yelwa Barde, Adamu Hussaini, Ifemedike Uzoma Grace, Bello Mohammed, and Abba Danjuma Mohammed. "Gene expression and its application in biotechnology." GSC Advanced Research and Reviews 8, no. 1 (2021): 012–21. https://doi.org/10.5281/zenodo.5113692.

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Advances in biotechnology has been the subject of praise for a decade now, this is due to techniques such as gene expression which has contributed immensely in the success of genetic engineering, medical advancement, vaccine production and agriculture. Gene expression has become a very important tool for the overall improvement of quality of life. This paper tries to look into the development of gene expression in the last forty years and to highlight how technological advancements in the study of gene expression brought about improvements as a focal point. Technological advancements associate

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17

Mora-Avilés, M. A. "EXPRESIÓN DE GENES RELACIONADOS CON LA PATOGENICIDAD EN PLANTAS DE BRÓCOLI EXPRESANDO EL GEN ENDOQUITINASA DE Trichoderma harzianum." Revista Chapingo Serie Horticultura X, no. 2 (2004): 141–46. http://dx.doi.org/10.5154/r.rchsh.2003.04.028.

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18

Ravindra and Prafulla Prakash Adkar Patil Dr. "Progress and future challenges in gene vectors, gene therapy systems and gene expressions." International Journal of Trends in Emerging Research and Development 2, no. 4 (2024): 114–19. https://doi.org/10.5281/zenodo.14618718.

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Despite these advancements, gene therapy faces several challenges. Immune responses to viral vectors, the potential for insertional mutagenesis, and difficulties in delivering the therapy to specific tissues are major hurdles that need to be addressed. Moreover, the high cost of gene therapy and ethical concerns surrounding genetic modifications continue to spark debate. Clinical outcomes have been promising, with successful treatments reported for conditions conditions like haemophilia, some cancers, and spinal muscular atrophy are examples. Nevertheless, in order to have a complete understan

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19

Galicia, J. C., B. R. Henson, J. S. Parker, and A. A. Khan. "Gene expression profile of pulpitis." Genes & Immunity 17, no. 4 (2016): 239–43. http://dx.doi.org/10.1038/gene.2016.14.

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20

Ruan, Xiaogang, Yingxin Li, Jiangeng Li, Daoxiong Gong, and Jinlian Wang. "Tumor-specific gene expression patterns with gene expression profiles." Science in China Series C 49, no. 3 (2006): 293–304. http://dx.doi.org/10.1007/s11427-006-0293-1.

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21

Purutçuoǧlu, Vilda. "Robust Gene Expression Index." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/182758.

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The frequentist gene expression index (FGX) was recently developed to measure expression on Affymetrix oligonucleotide DNA arrays. In this study, we extend FGX to cover nonnormal log expressions, specifically long-tailed symmetric densities and call our new index as robust gene expression index (RGX). In estimation, we implement the modified maximum likelihood method to unravel the elusive solutions of likelihood equations and utilize the Fisher information matrix for covariance terms. From the analysis via the bench-mark datasets and simulated data, it is shown that RGX has promising results

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22

P., Avila Clemenshia. "A Research on Cancer Subtype Classification Using Gene Expression Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 490–500. http://dx.doi.org/10.5373/jardcs/v12sp4/20201514.

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23

Venkataravanappa, J. T., K. C. Prasad, and S. Balakrishna. "LR4 gene expression in patients with chronic suppurative otitis media." Ukrainian Biochemical Journal 93, no. 6 (2021): 87–92. http://dx.doi.org/10.15407/ubj93.06.087.

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24

Ayakoroma, Faith Ajiebabhio Ogbole* and Joy Jacob. "Effect of Diet on Interleukin-2 Gene Expression and Hemacrit." International Journal of Research Publication and Reviews 5, no. 8 (2024): 1417–26. http://dx.doi.org/10.55248/gengpi.5.0824.2116.

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25

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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26

K, Sathishkumar, Balamurugan Dr., Dr Akpojaro Jackson, and Ramalingam M. "Efficient Clustering Methods and Statistical Approaches for Gene Expression Data." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (2019): 440–47. http://dx.doi.org/10.5373/jardcs/v11sp11/20193052.

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27

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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28

Zahn, Laura M. "Localizing gene expression." Science 373, no. 6550 (2021): 70.2–70. http://dx.doi.org/10.1126/science.373.6550.70-b.

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29

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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30

Frederickson, Robert. "Profiling gene expression." Nature Biotechnology 17, no. 8 (1999): 739. http://dx.doi.org/10.1038/11655.

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31

Stevens, Katherine. "Insulating gene expression." Nature Methods 5, no. 4 (2008): 284–85. http://dx.doi.org/10.1038/nmeth0408-284b.

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32

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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33

Whitchurch, A. K. "Gene expression microarrays." IEEE Potentials 21, no. 1 (2002): 30–34. http://dx.doi.org/10.1109/45.985325.

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34

Weitzman, Jonathan B. "Shaping gene expression." Genome Biology 3 (2002): spotlight—20020220–01. http://dx.doi.org/10.1186/gb-spotlight-20020220-01.

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35

Tsuchyia, Masa, Sum T. Wong, Zhen X. Yeo, et al. "Gene expression waves." FEBS Journal 274, no. 11 (2007): 2878–86. http://dx.doi.org/10.1111/j.1742-4658.2007.05822.x.

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36

Adler, E. M. "Activating Gene Expression." Science's STKE 2006, no. 362 (2006): tw392. http://dx.doi.org/10.1126/stke.3622006tw392.

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37

Orlowski, Michael. "Gene expression inMucordimorphism." Canadian Journal of Botany 73, S1 (1995): 326–34. http://dx.doi.org/10.1139/b95-263.

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An ongoing dialectic has concerned the relative importance of differential gene expression versus the pattern of new wall deposition in Mucor dimorphism. Numerous physiological processes and enzyme activities have been observed in flux during morphogenesis, but a causal link to dimorphism has been infrequently demonstrated. Very few of the proteins that are conspicuous in two-dimensional polyacrylamide gel electrophoresis are specific to cell morphology or significantly change in amount during morphogenesis. Cyclic AMP, putrescine, S-adenosylmethionine, and enzymes governing their intracellula

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38

Nitzan, Mor, Nikos Karaiskos, Nir Friedman, and Nikolaus Rajewsky. "Gene expression cartography." Nature 576, no. 7785 (2019): 132–37. http://dx.doi.org/10.1038/s41586-019-1773-3.

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39

Kuusisto, Finn. "Gene expression profiling." XRDS: Crossroads, The ACM Magazine for Students 21, no. 4 (2015): 71. http://dx.doi.org/10.1145/2788538.

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40

Robinson, Richard. "Quantitative gene expression." Genome Biology 4 (2003): spotlight—20030320–01. http://dx.doi.org/10.1186/gb-spotlight-20030320-01.

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41

Miller, W. Allen, S. P. Dinesh-Kumar, and Cynthia P. Paul. "Luteovirus Gene Expression." Critical Reviews in Plant Sciences 14, no. 3 (1995): 179–211. http://dx.doi.org/10.1080/07352689509701926.

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42

Graydon, Oliver. "Gene expression control." Nature Photonics 7, no. 11 (2013): 848. http://dx.doi.org/10.1038/nphoton.2013.294.

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43

Dundr, M., and T. Misteli. "Gene expression dynamics." Biomedicine & Pharmacotherapy 57, no. 3-4 (2003): 180. http://dx.doi.org/10.1016/s0753-3322(02)00347-5.

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44

Miller, W. A., S. P. Dinesh-Kumar, and C. P. Paul. "Luteovirus Gene Expression." Critical Reviews in Plant Sciences 14, no. 3 (1995): 179. http://dx.doi.org/10.1080/713608119.

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45

Jasny, B. R. "Solving Gene Expression." Science 306, no. 5696 (2004): 629. http://dx.doi.org/10.1126/science.306.5696.629.

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46

Purnell, B. A. "Specifying Gene Expression." Science Signaling 1, no. 6 (2008): ec59-ec59. http://dx.doi.org/10.1126/stke.16ec59.

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47

Arnosti, David. "Modeling gene expression." Methods 62, no. 1 (2013): 1–2. http://dx.doi.org/10.1016/j.ymeth.2013.08.001.

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48

Raetz, Elizabeth A., Philip J. Moos, Aniko Szabo, and William L. Carroll. "GENE EXPRESSION PROFILING." Hematology/Oncology Clinics of North America 15, no. 5 (2001): 911–30. http://dx.doi.org/10.1016/s0889-8588(05)70257-4.

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49

Helser, Terry L. "Gene Expression Wordsearch." Journal of Chemical Education 87, no. 4 (2010): 408. http://dx.doi.org/10.1021/ed800130v.

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50

Sotiriou, C., and P. Dinh. "Gene expression profiling." European Journal of Cancer Supplements 6, no. 7 (2008): 105. http://dx.doi.org/10.1016/s1359-6349(08)70508-1.

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