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The article presents a science experiment for school children to help them in understanding the science of genetics. A list of required materials including copies of the imaginary frier fly, and sheets of paper, is presented. Preparation instructions, specific procedures, and discussion topics are also presented.

Analyzing microarray data could help discover significant cancer genes and their mutual interactions, which can be used to generate hypothesis for the identification and validation of genetic biomarkers. However, the commonly used statistical significance analysis can only provide information of individual genes, thus neglecting influence of the mutual interactions. Therefore, methods aiming at discovering combinational gene signatures are highly valuable. In this paper, an integrated approach of support vector machine (SVM) and variable neighborhood search (VNS) is introduced in searching the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients. Cross validation results show that this method outperforms other existing algorithms. Further validation with the test dataset shows that only one of the fourteen samples is misclassified. The high testing accuracy further suggests that the proposed method has capability of extracting the stable discriminative signatures from microarray data. [ABSTRACT FROM AUTHOR]

Gene expression profiles have great potential as a medical diagnosis tool, since they represent the state of a cell at the molecular level. In the classification of cancer type research, available training data sets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed in order to improve predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this paper, we propose to combine genetic algorithms (GAs) and improved binary particle swarm optimization (IBPSO) for feature selection. PSO serves as a local optimizer each time the GA has been run for a single generation. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as an evaluator of the GA and IBPSO. The proposed method is applied and compared to six classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods. [ABSTRACT FROM AUTHOR]

RNA interference (RNAi) is a powerful genetic analysis tool for the efficient silencing of target genes and RNAi high throughput screening (HTS) is becoming an important tool in drug discovery and development. The application of RNAi HTS relies on good analytic methods for quality control and hit selection in RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed for measuring the magnitude of difference between two populations. For the use of SSMD in either QC or hit selection, we should determine the threshold of SSMD. To do so, we first present and prove a theorem that establishes the link between SSMD and d+-probability. Based on the original meaning of SSMD and its probability interpretation, we then propose the cutoff criteria for quality control and hit selection respectively and discuss how to apply these thresholds in RNAi HTS experiments. The link between SSMD and d+-probability and the SSMD thresholds constructed in this paper provide a basis for the use of SSMD in both quality control and hit selection in RNAi HTS experiment. [ABSTRACT FROM AUTHOR]

In this paper, we study a special class of nonbinary additive cyclic codes over GF(4) which we call reversible complement cyclic codes. Such codes are suitable for constructing codewords for DNA computing. We develop the theory behind constructing the set of generator polynomials for these codes. We study, as an example, all length—7 codes over GF(4) and list those that have the largest minimum Hamming distance and largest number of codewords. [ABSTRACT FROM AUTHOR]

Eating disorders and disordered eating symptoms are clearly heritable, but recent research has suggested that genetic and environmental influences on disordered eating symptoms vary as a function of gender and development. Data are limited, but evidence suggests that gender may moderate the type of genetic risk, rather than the magnitude of genetic effects, on disordered eating symptoms. Only a moderate proportion of the genetic influences on disordered eating symptoms are shared between males and females. In females, at least some of the unique genetic risk may be related to puberty and ovarian hormone regulation. The heritability of disordered eating symptoms in females increases with both pubertal development and increasing levels of estradiol. Although much more research is needed to elucidate specific mechanisms, gonadal hormones may be promising candidates for understanding sex and developmental effects and the ways in which genes exert their influence on disordered eating. [ABSTRACT FROM AUTHOR]

The article presents a lesson plan for scientific education which teaches concepts about the parts of a flower and also discusses importance of flower. It further focuses on various scientists who have studied about the mixing of genes in plants including Prospero Alpini, Carolus Linnaeus and Charles Darwin. It further discusses importance of plants and mentions that various plant species are endangered.

A definition of the term "Ras transformation targets," as used in cancer terminology, is presented. It refers to a set of genes deregulated by the expression of oncogenic Ras proteins. Target genes mediate the initiation of malignant transformation in normal cells and tissues and/or maintain the biological properties of cancer cells.