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Journal articles on the topic 'Gene selection'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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 (November 20, 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 were the optimal reference genes, both EF1α and 28S were suitable for experiments of different tissues, whereas for herbicide treatments, GAPDH and ACT were suitable for normalizations of expression data. In addition, GAPDH and EF1α were the suitable reference genes.

2

R, Dr Prema. "Feature Selection for Gene Expression Data Analysis – A Review." International Journal of Psychosocial Rehabilitation 24, no. 5 (May 25, 2020): 6955–64. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020695.

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3

Lee, K. E., N. Sha, E. R. Dougherty, M. Vannucci, and B. K. Mallick. "Gene selection: a Bayesian variable selection approach." Bioinformatics 19, no. 1 (January 1, 2003): 90–97. http://dx.doi.org/10.1093/bioinformatics/19.1.90.

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4

Klee, Eric W., Stephen C. Ekker, and Lynda B. M. Ellis. "Target selection forDanio rerio functional genomics." genesis 30, no. 3 (2001): 123–25. http://dx.doi.org/10.1002/gene.1045.

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5

Tsakas, SC. "Species versus gene selection." Genetics Selection Evolution 21, no. 3 (1989): 247. http://dx.doi.org/10.1186/1297-9686-21-3-247.

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6

Greenspan, R. J. "Selection, Gene Interaction, and Flexible Gene Networks." Cold Spring Harbor Symposia on Quantitative Biology 74 (January 1, 2009): 131–38. http://dx.doi.org/10.1101/sqb.2009.74.029.

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7

D., Saravanakumar. "Improving Microarray Data Classification Using Optimized Clustering-Based Hybrid Gene Selection Algorithm." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 486–95. http://dx.doi.org/10.5373/jardcs/v12sp3/20201283.

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8

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 (May 30, 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 chemistry. The expression of stability of the used reference genes was calculated using the geNorm application. Different gene expression profiles among the age categories of pigs were found out. RPS18 has been identified as the gene with the most stable expression in the muscle tissue of all pig age categories. HPRT1 and RPL32 were found to have the highest stability in piglets and adult pigs, and in foetuses and adults pigs, respectively. The newly used reference gene, TAF4B, reached the highest expression stability in piglets.

9

Gilad, Yoav, Alicia Oshlack, and Scott A. Rifkin. "Natural selection on gene expression." Trends in Genetics 22, no. 8 (August 2006): 456–61. http://dx.doi.org/10.1016/j.tig.2006.06.002.

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10

Behar, Hilla, and Marcus W. Feldman. "Gene-culture coevolution under selection." Theoretical Population Biology 121 (May 2018): 33–44. http://dx.doi.org/10.1016/j.tpb.2018.03.001.

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11

Greenman, Chris D. "Haploinsufficient Gene Selection in Cancer." Science 337, no. 6090 (July 5, 2012): 47–48. http://dx.doi.org/10.1126/science.1224806.

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12

Knowlton, N., I. Dozmorov, K. D. Kyker, R. Saban, C. Cadwell, M. B. Centola, and R. E. Hurst. "Template-driven gene selection procedure." IEE Proceedings - Systems Biology 153, no. 1 (2006): 4. http://dx.doi.org/10.1049/ip-syb:20050020.

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13

Gould, J., G. Getz, S. Monti, M. Reich, and J. P. Mesirov. "Comparative gene marker selection suite." Bioinformatics 22, no. 15 (May 18, 2006): 1924–25. http://dx.doi.org/10.1093/bioinformatics/btl196.

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14

Wiliński, Artur, and Stanisław Osowski. "Gene selection for cancer classification." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 28, no. 1 (January 2, 2009): 231–41. http://dx.doi.org/10.1108/03321640910919020.

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15

Nitovska, I. O., B. V. Morgun, O. Ye Abraimova, and T. M. Satarova. "Glyphosate selection of maize transformants containing CP4epsps gene." Faktori eksperimental'noi evolucii organizmiv 26 (September 1, 2020): 239–44. http://dx.doi.org/10.7124/feeo.v26.1273.

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Aim. To study the selection conditions of maize transformants containing the CP4epsps gene using glyphosate as a selective agent. Methods. Tissue culture in vitro, Agrobacterium-mediated transformation, selection of transgenic plants, isolation of total plant DNA, analysis of plant DNA by polymerase chain reaction (PCR). Results. The morphogenic maize callus of immature embryos of the hybrid (PLS61)R2×PLS61 was produced, which had a high regeneration rate (up to 95%), that persisted over long cultivation. Agrobacterium mediated transformation of the morphogenic callus and selection of the transgenic material using glyphosate yielded maize transformants containing the CP4epsps gene at a frequency of 1%. Conclusions. Maize genotype (PLS61)R2×PLS61 is promising for studies on the maize genetic transformation, in particular for the production of transgenic maize resistant to glyphosate herbicide. The use of morphogenic maize callus (PLS61)R2×PLS61 and glyphosate as a selective agent at a concentration of 0.1 mM and 0.25 mM in media for callusogenesis and 0.01 mM in the medium for regeneration was effective for the selection of transgenic plants with the gene CP4epsps. Keywords: Zea mays L., morphogenic callus, Agrobacterium-mediated transformation, PCR, genetic engineering.

16

Cherry, Joshua L. "Selection-Driven Gene Inactivation in Salmonella." Genome Biology and Evolution 12, no. 3 (February 11, 2020): 18–34. http://dx.doi.org/10.1093/gbe/evaa010.

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Abstract Bacterial genes are sometimes found to be inactivated by mutation. This inactivation may be observable simply because selection for function is intermittent or too weak to eliminate inactive alleles quickly. Here, I investigate cases in Salmonella enterica where inactivation is instead positively selected. These are identified by a rate of introduction of premature stop codons to a gene that is higher than expected under selective neutrality, as assessed by comparison to the rate of synonymous changes. I identify 84 genes that meet this criterion at a 10% false discovery rate. Many of these genes are involved in virulence, motility and chemotaxis, biofilm formation, and resistance to antibiotics or other toxic substances. It is hypothesized that most of these genes are subject to an ongoing process in which inactivation is favored under rare conditions, but the inactivated allele is deleterious under most other conditions and is subsequently driven to extinction by purifying selection.

17

Liu, Changlu, Jianzhong Ma, and Christopher I. Amos. "Bayesian variable selection for hierarchical gene–environment and gene–gene interactions." Human Genetics 134, no. 1 (August 26, 2014): 23–36. http://dx.doi.org/10.1007/s00439-014-1478-5.

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18

Kosoy, R., M. Ransom, H. Chen, M. Marconi, F. Macciardi, N. Glorioso, P. K. Gregersen, D. Cusi, and M. F. Seldin. "Evidence for malaria selection of a CR1 haplotype in Sardinia." Genes & Immunity 12, no. 7 (May 19, 2011): 582–88. http://dx.doi.org/10.1038/gene.2011.33.

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19

Xiong, Momiao, Wuju Li, Jinying Zhao, Li Jin, and Eric Boerwinkle. "Feature (Gene) Selection in Gene Expression-Based Tumor Classification." Molecular Genetics and Metabolism 73, no. 3 (July 2001): 239–47. http://dx.doi.org/10.1006/mgme.2001.3193.

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20

Yang, Dong, and Xuchang Zhu. "Gene Correlation Guided Gene Selection for Microarray Data Classification." BioMed Research International 2021 (August 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/6490118.

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The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

21

Tomlinson, Ian P. M. "Major-Gene Models of Sexual Selection Under Cyclical Natural Selection." Evolution 42, no. 4 (July 1988): 814. http://dx.doi.org/10.2307/2408872.

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22

Tomlinson, Ian P. M. "MAJOR-GENE MODELS OF SEXUAL SELECTION UNDER CYCLICAL NATURAL SELECTION." Evolution 42, no. 4 (July 1988): 814–16. http://dx.doi.org/10.1111/j.1558-5646.1988.tb02499.x.

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23

Delrue, Iris, Qiubao Pan, Anna K. Baczmanska, Bram W. Callens, and Lia L. M. Verdoodt. "Determination of the Selection Capacity of Antibiotics for Gene Selection." Biotechnology Journal 13, no. 8 (March 23, 2018): 1700747. http://dx.doi.org/10.1002/biot.201700747.

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24

Mundra, Piyushkumar A., and Jagath C. Rajapakse. "Gene and sample selection using T-score with sample selection." Journal of Biomedical Informatics 59 (February 2016): 31–41. http://dx.doi.org/10.1016/j.jbi.2015.11.003.

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25

Šliková, S., E. Gregorová, P. Bartoš, and J. Kraic. "Marker-assisted selection for leaf rust resistance in wheat by transfer of gene Lr19." Plant Protection Science 39, No. 1 (November 11, 2011): 13–17. http://dx.doi.org/10.17221/3821-pps.

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Cultivar Agrus, possessing a chromosomal substitution, and cultivar Sunnan, possessing a translocation from Thinopyrum ponticum (= Agropyron elongatum, 2n = 10x) with leaf rust resistance gene Lr19 against Puccinia triticina, were crossed with the susceptible winter wheat cultivars Sofia, Simona and Lívia to transfer Lr19 into agronomically better genotypes by marker-assisted selection. Altogether 304 individuals of the F2 progeny were screened for endopeptidase phenotypes. We found null endopeptidase allele Ep-D1c (marker tightly liked with resistance gene Lr19) in 49 plants. The progenies of 40 plants of the F2 generation (with Ep-D1c) were reselected with the same marker and tested for leaf rust reaction. Results achieved with the isozyme marker corresponded with those of the resistance tests. We obtained 28 F3 families with resistance gene Lr19 confirmed by presence of the null endopeptidase allele and by tests for leaf rust reaction. Field tests showed that Agrus increased the height of plants in the progenies, and the smallest negative effect on yield components was observed in both crosses with cultivar Sunnan.  

26

Burke, John, Hui Wang, Winston Hide, and Daniel B. Davison. "Alternative Gene Form Discovery and Candidate Gene Selection from Gene Indexing Projects." Genome Research 8, no. 3 (March 1, 1998): 276–90. http://dx.doi.org/10.1101/gr.8.3.276.

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27

Liu, Huawen, Lei Liu, and Huijie Zhang. "Ensemble gene selection for cancer classification." Pattern Recognition 43, no. 8 (August 2010): 2763–72. http://dx.doi.org/10.1016/j.patcog.2010.02.008.

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28

Kumaresan, P. K. "Feature Selection Clustering for Gene Data." International Journal of Emerging Research in Management and Technology 6, no. 9 (June 24, 2018): 183. http://dx.doi.org/10.23956/ijermt.v6i9.107.

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Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this paper , an algorithm is proposed which makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solution in both clustering and feature selection. The proposed method uses genetic algorithm to preserve the population diversity and prevent premature convergence. The algorithm is implemented in Matlab 7.4 under windows operating system. The results show that the proposed algorithm outperforms existing algorithms in terms of accuracy.

29

Wei Zhao, a., Gang Wang, b., Hongbin Wang, c., Huiling Chen, et al. "A Novel Framework for Gene Selection." International Journal of Advancements in Computing Technology 3, no. 3 (April 30, 2011): 184–91. http://dx.doi.org/10.4156/ijact.vol3.issue3.18.

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30

Wang, Hong-Qiang, and De-Shuang Huang. "Regulation probability method for gene selection." Pattern Recognition Letters 27, no. 2 (January 2006): 116–22. http://dx.doi.org/10.1016/j.patrec.2005.07.007.

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31

Latkowski, Tomasz, and Stanislaw Osowski. "Gene selection in autism – Comparative study." Neurocomputing 250 (August 2017): 37–44. http://dx.doi.org/10.1016/j.neucom.2016.08.123.

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32

Koskiniemi, Sanna, Song Sun, Otto G. Berg, and Dan I. Andersson. "Selection-Driven Gene Loss in Bacteria." PLoS Genetics 8, no. 6 (June 28, 2012): e1002787. http://dx.doi.org/10.1371/journal.pgen.1002787.

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33

Murrell, Ben, Steven Weaver, Martin D. Smith, Joel O. Wertheim, Sasha Murrell, Anthony Aylward, Kemal Eren, et al. "Gene-Wide Identification of Episodic Selection." Molecular Biology and Evolution 32, no. 5 (February 19, 2015): 1365–71. http://dx.doi.org/10.1093/molbev/msv035.

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34

Barresi, John. "Group selection and “the pious gene”." Behavioral and Brain Sciences 19, no. 4 (December 1996): 777–78. http://dx.doi.org/10.1017/s0140525x00043995.

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AbstractIf selection at the group level is to be considered more than a mere possibility, it is important to find phenomena that are best explained at this level of selection. I argue that human religious phenomena provide evidence for the selection of a “pious gene” at the group level, which results in a human tendency to believe in a transcendental reality that encourages behavioral conformity to collective as opposed to individual interest.

35

Masulli, Francesco, and Stefano Rovetta. "Random Voronoi ensembles for gene selection." Neurocomputing 55, no. 3-4 (October 2003): 721–26. http://dx.doi.org/10.1016/s0925-2312(03)00377-1.

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36

Armarego-Marriott, Tegan. "Climatic selection and gene expression plasticity." Nature Climate Change 11, no. 1 (January 2021): 4. http://dx.doi.org/10.1038/s41558-020-00979-3.

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37

GRAF, DANIEL, AMANDA G. FISHER, and MATTHIAS MERKENSCHLAGER. "Selection-induced gene expression in thymocytes." Genetical Research 70, no. 1 (August 1997): 79–89. http://dx.doi.org/10.1017/s0016672397352860.

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38

K, Anitha. "Gene Selection Based on Rough Set." INTERNATIONAL JOURNAL OF COMPUTING ALGORITHM 1, no. 2 (December 15, 2012): 38–41. http://dx.doi.org/10.20894/ijcoa.101.001.002.004.

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39

Lawrence, Jeffrey G. "Gene Organization: Selection, Selfishness, and Serendipity." Annual Review of Microbiology 57, no. 1 (October 2003): 419–40. http://dx.doi.org/10.1146/annurev.micro.57.030502.090816.

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40

Hutchinson, Lisa. "NK gene donor selection in AML." Nature Reviews Clinical Oncology 8, no. 1 (December 22, 2010): 3. http://dx.doi.org/10.1038/nrclinonc.2010.197.

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41

Rajapakse, Jagath C., and Piyushkumar A. Mundra. "Multiclass Gene Selection Using Pareto-Fronts." IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, no. 1 (January 2013): 87–97. http://dx.doi.org/10.1109/tcbb.2013.1.

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42

Islam, A. K. M. Tauhidul, Byeong-Soo Jeong, A. T. M. Golam Bari, Chae-Gyun Lim, and Seok-Hee Jeon. "MapReduce based parallel gene selection method." Applied Intelligence 42, no. 2 (July 30, 2014): 147–56. http://dx.doi.org/10.1007/s10489-014-0561-x.

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43

Filippone, Maurizio, Francesco Masulli, and Stefano Rovetta. "Simulated annealing for supervised gene selection." Soft Computing 15, no. 8 (March 31, 2010): 1471–82. http://dx.doi.org/10.1007/s00500-010-0597-8.

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44

Pei, Shun, and De-Shuang Huang. "Cooperative Competition Clustering for Gene Selection." Journal of Cluster Science 17, no. 4 (October 4, 2006): 637–51. http://dx.doi.org/10.1007/s10876-006-0077-6.

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45

ZHOU, XIN, and K. Z. MAO. "REGULARIZATION NETWORK-BASED GENE SELECTION FOR MICROARRAY DATA ANALYSIS." International Journal of Neural Systems 16, no. 05 (October 2006): 341–52. http://dx.doi.org/10.1142/s0129065706000743.

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Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.

46

Amills, M., O. Ramírez, A. Tomàs, G. Obexer-Ruff, and O. Vidal. "Positive selection on mammalian MHC-DQ genes revisited from a multispecies perspective." Genes & Immunity 9, no. 8 (August 7, 2008): 651–58. http://dx.doi.org/10.1038/gene.2008.62.

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47

Omara, Hicham, Mohamed Lazaar, and Youness Tabii. "Effect of Feature Selection on Gene Expression Datasets Classification Accurac." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3194. http://dx.doi.org/10.11591/ijece.v8i5.pp3194-3203.

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<span>Feature selection attracts researchers who deal with machine learning and data mining. It consists of selecting the variables that have the greatest impact on the dataset classification, and discarding the rest. This dimentionality reduction allows classifiers to be fast and more accurate. This paper traits the effect of feature selection on the accuracy of widely used classifiers in literature. These classifiers are compared with three real datasets which are pre-processed with feature selection methods. More than 9% amelioration in classification accuracy is observed, and k-means appears to be the most sensitive classifier to feature selection.</span>

48

MUKHERJEE, SACH, and STEPHEN J. ROBERTS. "A THEORETICAL ANALYSIS OF THE SELECTION OF DIFFERENTIALLY EXPRESSED GENES." Journal of Bioinformatics and Computational Biology 03, no. 03 (June 2005): 627–43. http://dx.doi.org/10.1142/s0219720005001211.

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A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.

49

Bhola, Abhishek, and Shailendra Singh. "Gene Selection Using High Dimensional Gene Expression Data: An Appraisal." Current Bioinformatics 13, no. 3 (May 3, 2018): 225–33. http://dx.doi.org/10.2174/1574893611666160610104946.

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50

Chen, Chien-Ming, Yu-Lun Lu, Chi-Pong Sio, Guan-Chung Wu, Wen-Shyong Tzou, and Tun-Wen Pai. "Gene Ontology based housekeeping gene selection for RNA-seq normalization." Methods 67, no. 3 (June 2014): 354–63. http://dx.doi.org/10.1016/j.ymeth.2014.01.019.

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