Results 31 to 40 of about 1,119,682 (262)
Species versus gene selection [PDF]
Genetics Selection Evolution, 1989 Author(s): Tsakas, S. C. | Abstract: Species selection has been recently promoted (Gould a Eldredge, 1988a, 1988b) as the driving force in macroevolution, and viewed as an explanation for the variability in rates observed, both temporally and spatially, at the phenotypic level. This has rekindled the contention between microevolutionists (Maynard Smith,
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Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies
Frontiers in Genetics, 2018 Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated
Stephane Wenric +2 more
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A Gene Selection Approach based on Clustering for Classification Tasks in Colon Cancer
Advances in Distributed Computing and Artificial Intelligence Journal, 2016 Gene selection (GS) is an important research area in the analysis of DNA-microarray data, since it involves gene discovery meaningful for a particular target annotation or able to discriminate expression profiles of samples coming from different ...
José Antonio CASTELLANOS GARZÓN +1 more
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Genes, individuals, and kin selection [PDF]
Proceedings of the National Academy of Sciences, 1981 The altruistic-gene theory of kin selection requires conditions so improbable that its reality is doubtful. The gene-quantity theory, including the theory of inclusive fitness, assumes that selection acts on sums of kins' genes, but no effective mechanism is apparent.
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IMPROVED GENE SELECTION FOR CLASSIFICATION OF MICROARRAYS [PDF]
Biocomputing 2003, 2002 In this paper we derive a method for evaluating and improving techniques for selecting informative genes from microarray data. Genes of interest are typically selected by ranking genes according to a test-statistic and then choosing the top k genes. A problem with this approach is that many of these genes are highly correlated.
Jäger, J., Sengupta, R., Ruzzo, W.
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Biomarker Gene Signature Discovery Integrating Network Knowledge
Biology, 2012 Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine.
Holger Fröhlich, Yupeng Cun
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A Gene Selection Method for Cancer Classification [PDF]
Computational and Mathematical Methods in Medicine, 2012 This paper proposes a method to select a set of genes from a large number of genes with the ability of classifying types of diseases. The proposed gene selection method is designed according to correlation analysis and the concept of 95% reference range. The method is very simple and uses the information of all genes.
Xiaodong Wang 0003, Jun Tian
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Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem
Mathematics, 2022 Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset
Salam Salameh Shreem +3 more
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New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile
IEEE Access, 2019 Several hybrid gene selection algorithms for cancer classification that employ bio-inspired evolutionary wrapper algorithm have been proposed in the literature and show good classification accuracy.
Nada Almugren, Hala M. Alshamlan
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Natural selection and gene substitution
Genetical Research, 1969 Using models which describe the change, by natural selection, of the actual numbers of genes rather than their relative frequencies, it is demonstrated that the equation familiar to geneticists, i.e.dp/dt=sp(1 −p), is appropriate under a wide range of circumstances.
M, Kimura, J F, Crow
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