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Journal articles on the topic 'Gene selection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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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2

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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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 (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

Hicham, Omara, Lazaar Mohamed, and Tabii Youness. "Effect of Feature Selection on Gene Expression Datasets Classification Accuracy." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3194–203. https://doi.org/10.11591/ijece.v8i5.pp3194-3203.

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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

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6

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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7

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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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 (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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9

V, Sudha, and Girijamma H. A. "SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fuzzy Cluster based Nearest Neighbor Classifier." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4505–18. https://doi.org/10.11591/ijece.v8i6.pp4505-4518.

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In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier

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10

Kaviani, Mina, Paul H. Goodwin, and David M. Hunter. "Differences in Gene Expression of Pear Selections Showing Leaf Curling or Leaf Reddening Symptoms Due to Pear Decline Phytoplasma." Plants 11, no. 3 (2022): 427. http://dx.doi.org/10.3390/plants11030427.

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While host gene expression has been related to symptoms associated with different phytoplasma diseases, it is unknown why some phytoplasmas are associated with different symptoms in genotypes of the same plant species. Pear tree selections showed symptoms of either leaf reddening (selection 8824-1) or leaf curling (selection 9328-1) associated with pear decline (PD) phytoplasma presence. PD populations were similar in leaves and shoots of the two selections, but in the roots, populations were significantly lower in selection 8824-1 than in 9328-1, indicating greater resistance. For host carboh

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11

Korolev, Konstantin. "Morphophysiological test of Linum usitatissimum L. under conditions of different levels of chloride salinity." АгроЭкоИнфо 5, no. 65 (2024): 19. http://dx.doi.org/10.51419/202145519.

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The selection of resistant varieties under conditions of abiotic stress is an urgent direction in the adaptive selection of flax. The presented work reflects the assessment of the phenotypic variability of 12 hybrid combinations of flax-longshanks according to the morphophysiological parameters of seedlings under the action of chloride salinization of two levels (E1, E2). The genotypes of flax of the fourth (F4) – fifth (F5) generations were used as objects of research. Significant differences (p>0.05, p>0.01, p>0.001) between hybrid populations and media were revealed for most of the

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12

Knowlton, N., I. Dozmorov, K. D. Kyker, et al. "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

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

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14

Bader, Ralf. "Gene Editing vs. Genetic Selection." American Journal of Bioethics 24, no. 8 (2024): 31–34. http://dx.doi.org/10.1080/15265161.2024.2361883.

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15

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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16

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

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17

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 (2009): 231–41. http://dx.doi.org/10.1108/03321640910919020.

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18

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

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19

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 (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 fun

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20

Ye, Xiucai, and Tetsuya Sakurai. "Unsupervised Feature Selection for Microarray Gene Expression Data Based on Discriminative Structure Learning." JUCS - Journal of Universal Computer Science 24, no. (6) (2018): 725–41. https://doi.org/10.3217/jucs-024-06-0725.

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The analysis of microarray gene expression data to obtain useful information is a challenging problem in bioinformatics. Feature selection is an efficient computational technique in processing the analysis of high-dimensional microarray data. Due to the lack of label information in practice, unsupervised feature selection is considered to be more practically important and correspondingly more difficult. In this paper, we propose a novel unsupervised feature selection method, which utilizes local regression and discriminant analysis for structure learning on microarray gene expression data. By

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21

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

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22

Fang, Yan, Yonghua Lin, Chuanbo Huang, and Zhaowen Li. "Gene Selection Algorithms in a Single-Cell Gene Decision Space Based on Self-Information." Mathematics 13, no. 11 (2025): 1829. https://doi.org/10.3390/math13111829.

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A critical step for gene selection algorithms using rough set theory is the establishment of a gene evaluation function to assess the classification ability of candidate gene subsets. The concept of dependency in a classic neighborhood rough set model plays the role of this evaluation function. This criterion only notes the information provided by the lower approximation and omits the upper approximation, which may result in the loss of some important information. This paper proposes gene selection algorithms within a single-cell gene decision space by employing self-information, taking into a

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23

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

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24

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 (2020): 486–95. http://dx.doi.org/10.5373/jardcs/v12sp3/20201283.

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25

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 (2001): 239–47. http://dx.doi.org/10.1006/mgme.2001.3193.

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26

Hayder, Adnan Saleh, Amer Sattar Rana, Mohammed Hussein Saeed Enas, and Saad Abdul-Zahra Dalael. "Hybrid features selection method using random forest and meerkat clan algorithm." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 5 (2022): 1046–54. https://doi.org/10.12928/telkomnika.v20i5.23515.

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In the majority of gene expression investigations, selecting relevant genes for sample classification is considered a frequent challenge, with researchers attempting to discover the minimum feasible number of genes while yet achieving excellent predictive performance. Various gene selection methods employ univariate (gene-by-gene) gene relevance rankings as well as arbitrary thresholds for selecting the number of genes, are only applicable to 2-class problems and use gene selection ranking criteria unrelated to the algorithm of classification. A modified random forest (MRF) algorithm depending

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27

Kosoy, R., M. Ransom, H. Chen, et al. "Evidence for malaria selection of a CR1 haplotype in Sardinia." Genes & Immunity 12, no. 7 (2011): 582–88. http://dx.doi.org/10.1038/gene.2011.33.

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28

Qiu, Feng, Pan Zheng, Ali Asghar Heidari, et al. "Mutational Slime Mould Algorithm for Gene Selection." Biomedicines 10, no. 8 (2022): 2052. http://dx.doi.org/10.3390/biomedicines10082052.

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A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which

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29

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 (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

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30

Kang, K. S., B. H. Cheon, S. U. Han, C. S. Kim, and W. Y. Choi. "Genetic Gain and Diversity under Different Selection Methods in a Breeding Seed Orchard of Quercus serrata." Silvae Genetica 56, no. 1-6 (2007): 277–81. http://dx.doi.org/10.1515/sg-2007-0039.

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Abstract Genetic gain and diversity were estimated in a 13- year old Quercus serrata breeding seed orchard under three selection (rouging) methods. The selections were based on individual selection, family selection, and family plus within family selection. Genetic gain was for stem volume and gene diversity was estimated by status number concept. Both estimated genetic gain and gene diversity were compared to those before selection and among selection scenarios. Estimated genetic gain for tree volume ranged from 4.0% to 9.1% for three selection methods under 50% selection intensity. Individua

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31

Su, Qiang, Yina Wang, Xiaobing Jiang, Fuxue Chen, and Wen-cong Lu. "A Cancer Gene Selection Algorithm Based on the K-S Test and CFS." BioMed Research International 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1645619.

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Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subse

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32

ZHOU, XIN, and K. Z. MAO. "REGULARIZATION NETWORK-BASED GENE SELECTION FOR MICROARRAY DATA ANALYSIS." International Journal of Neural Systems 16, no. 05 (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 i

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33

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

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34

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

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35

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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36

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 (2018): 1700747. http://dx.doi.org/10.1002/biot.201700747.

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37

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 (1998): 276–90. http://dx.doi.org/10.1101/gr.8.3.276.

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38

Miles, James. "Unnatural Selection." Philosophy 73, no. 4 (1998): 593–608. http://dx.doi.org/10.1017/s0031819198004057.

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This paper shows how the last twenty-five years of vocal human Darwinism (human sociobiology and evolutionary psychology) directly rejects the ‘selfish gene’ theory it is supposedly based upon. ‘Evangelistic sociobiology’, as Dawkins has called it, argues that humans evolved to be ‘the altruistic ape’. Using selfish gene theory this paper shows that we are born just another selfish ape. Given the ‘gross immorality’ (George Williams) of natural selection, one implication is that modern genetics has yet to face up to our true genetic code. The ultimate conclusion of this paper is that culture ma

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39

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

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40

Kumaresan, P. K. "Feature Selection Clustering for Gene Data." International Journal of Emerging Research in Management and Technology 6, no. 9 (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 sh

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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

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49

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

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50

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

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