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The purpose of this paper is to discover the most important genes generated by the gene expression arrays, responsible for the recognition of particular types of cancer.

The paper presents the analysis of different techniques of gene selection, including correlation, statistical hypothesis, clusterization and linear support vector machine (SVM).

The correctness of the gene selection is proved by mapping the distribution of selected genes on the two‐coordinate system formed by two most important principal components of the PCA transformation. Final confirmation of this approach are the classification results of recognition of several types of cancer, performed using Gaussian kernel SVM.

The results of selection of the most significant genes used for the SVM recognition of seven types of cancer have confirmed good accuracy of results. The presented methodology is of potential use in practical application in bioinformatics.

The chapter analyses the re-emergence of gene editing as an object of policy attention at the European Union (EU) level. Editing the genome of plants and/or animals has been a rather controversial component of all EU policies on agricultural biotechnology since the late 1980s. The chapter examines in detail the various initiatives that have been assumed for the regulation of gene editing at the EU level. Since the first political and legislative attempts, the field has been revolutionized with the development of the CRISPR-Cas9 system, which is comparatively much easier to design, produce, and use. Beyond the pure, safety-driven scientific questions, gene editing, in its contemporary form, raises a series of ethical and regulatory questions that are discussed in the context of the legal options and competences of the EU legislators. Special attention is paid to questions about the legal status of gene editing in Europe and the adequacy of the current GMO framework to deal with all the challenges associated with the latest scientific developments in the field of gene editing with a special focus on gene drive. Given the ongoing discussions regarding the ethical tenets of gene editing, the chapter investigates the question on whether there is a need to shape an EU-wide “intervention” that will address the complex and dynamic socio-ethical challenges of gene editing and puts forward a series of proposals for the framing of an inclusive framework that will be based on the need to re-enforce public trust in the EU governance of emerging technologies.

National differences in the demand for voluntary external audits have been linked to multiple factors, such as differences in a country's rate of growth, access to external credit, and institutional quality. Audits, however, also have a psychological cost, whose intensity is genetically and culturally hereditary. Using a sample of 3,072 private firms across 34 industries in seven countries, including five countries or regions from the former Soviet Comecon, we find that a country's share of firms choosing to undergo external audits is negatively related to the prevalence of carriers of the G allele in the mu-opioid receptor gene's A118G polymorphism, also known as the “social sensitivity” gene. Furthermore, the relationship between the prevalence of the social sensitivity gene and audits is fully mediated by a national culture's degree of collectivism. The results are statistically and economically highly significant and remain robust to the introduction of a set of confounding factors at the firm and country levels. Our results have practical relevance in recognizing psychological diversity when conducting audits and, more generally, preventing burnout in the workplace.

The products of transgenic technology have captured the attention of enthusiasts and detractors, but transgenics are just one tool of agricultural biotechnology. Other applications enable scientists to understand biodiversity, to track genes through generations in breeding programs, and to move genes among closely related as well as unrelated organisms. These applications all have the potential to lead to substantial productivity gains.

In this chapter we provide an introduction to basic plant genetic concepts, defining molecular markers, transgenic and cisgenic techniques. We briefly summarize the status of commercialized biotechnology applications to agriculture. We consider the likely future commercialization of products like drought tolerant crops, crops designed to improve human nutrition, pharmaceuticals from transgenic plants, biofuels, and crops for environmental remediation. We identify genomic selection as a potentially powerful new technique and conclude with our reflections on the state of agricultural biotechnology.

Research at universities and other public-sector institutions, largely focused on advancing knowledge, has aroused enormous optimism about the promise of these DNA-based technologies. This in turn has led to large private-sector investments on maize, soybean, canola, and cotton, with wide adoption of the research products in about eight countries. Much has been made of the potential of biotechnology to address food needs in the low-income countries, and China, India, and Brazil have large public DNA-based crop variety development efforts. But other lower income developing countries have little capability to use these tools, even the most straightforward marker applications. Ensuring that these and other applications of biotechnology lead to products that are well adapted to local agriculture requires adaptive research capacity that is lacking in the lowest income, most food-insecure nations. We are less optimistic than many others that private research will fund these needs.

Heritability studies attempt to estimate the contribution of genes (vs. environments) to variation in phenotypes (or outcomes of interest) in a given population at a given time. This chapter scrutinizes heritability studies of adverse health phenotypes, emphasizing flaws that have become more glaring in light of recent advances in the life sciences and manifest most visibly in epigenetics.

Drawing on a diverse body of research and critical scholarship, this chapter examines the veracity of methodological and conceptual assumptions of heritability studies.

The chapter argues that heritability studies are futile for two reasons: (1) heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and likely biased toward inflated heritability, and, more importantly (2) the conceptual (biological) model on which heritability studies depend – that of identifiably separate effects of genes versus the environment on phenotype variance – is unsound. As discussed, contemporary bioscientific work indicates that genes and environments are enmeshed in a complex (bidirectional, interactional), dynamic relationship that defies any attempt to demarcate separate contributions to phenotype variance. Thus, heritability studies attempt the biologically impossible. The emerging research on the importance of microbiota is also discussed, including how the commensal relationship between microbial and human cells further stymies heritability studies.

Understandably, few sociologists have the time or interest to be informed about the methodological and theoretical underpinnings of heritability studies or to keep pace with the incredible advances in genetics and epigenetics over the last several years. The present chapter aims to provide interested scholars with information about heritability and heritability estimates of adverse health outcomes in light of recent advances in the biosciences.

Purpose – The goal of this chapter is to demonstrate the importance of incorporating gene by environment (G×E) interactions into behavioral science theory and research.

Design/methodology/approach – We critique behavioral genetics, discuss the emergence of epigenetics, review findings on G×E effects, and present the differential susceptibility model of gene–environment interplay.

Findings – The studies reviewed demonstrate that genetic variation often interacts with environmental context to influence the probability of various behaviors. Importantly, in many, and perhaps most, of the studies reviewed, the genetic variable, unlike the environmental variable, has little if any main effect on the outcome of interest. Rather, the influence of the genetic variable is limited to its moderation of the effect of the environmental construct.

Research limitations/implications – Molecular G×E research does not undermine the importance of environmental factors; rather it shows how social scientific explanations of human behavior might be made more precise by incorporating genetic information. This suggests expanded research opportunities for those interested in social causation.

Social implications – This model of molecular G×E research presented suggests that a substantial proportion of the population is genetically predisposed to be more susceptible than others to environmental influence. We argue that this model of G×E is particularly relevant to sociologists and psychologists and has the potential to enhance the development of theory in both areas.

Originality/value – This chapter will be of particular interest to sociologists and psychologists who have found the behavioral genetic paradigm off-putting because of its emphasis on genetic main effects and genetic determinism. The current chapter offers an alternative model that may better capture the available data and better integrate social processes with genetic and biological processes.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

Describes Myriad Genetics and its struggle to develop a genetic testing service while facing challenges from competitors and activist organizations. After Myriad's discovery of the BRCA gene, capable of genetic testing for breast cancer in women, Myriad needed to choose a strategy to provide this service to the public. With several major competitors offering similar services, intense media scrutiny, and a charged activist and political climate, a poor Myriad decision could have major repercussions.

Genetic engineering offers an opportunity to improve aspects of the agronomic performance, resistance to pests and pathogens and end use quality of crops by inserting specific genes. Discusses the basic principles and procedures of plant genetic engineering, including the use of particle bombardment for delivery of genes into regenerable tissues. Also discusses how this technology can be used to alter the level (up or down‐regulation) or pattern of expression of endogenous genes, or to insert novel activities or properties by inserting genes from other sources (other plants, animals or microbes). Finally, describes work in progress in our own laboratories on the improvement of the bread‐ making quality of wheat by manipulating the amount and composition of the HMW subunits of glutenin.

Improvement of the environment quality and human development has become the main focus of modern urban development. Micro-renewal is a relatively people-oriented model of urban transformation compared with traditional renewal modes. To improve the theoretical system of neighborhood micro-renewal from a microcosmic perspective, a comprehensive analysis of neighborhood residents' cognition is needed. The purpose of the study is to explore the possibilities and methods of applying gene theory into the study of neighborhood micro-renewal.

According to the meme theory, the research explores the genetic analysis of neighborhood micro-renewal. The cross-over studies with “gene theory” from natural science to social science are analyzed and the neighborhood micro-renewal system was constructed from the perspective of micro-participants and micro-objects. Moreover, the concept of neighborhood “micro-renewal gene” was put forward. Finally, the authors show three application scenarios of public participation with a specific neighborhood micro-renewal project.

The cross research on urban studies with gene theory could be divided into three scales and four research fields. The characteristics of carrying out neighborhood micro-renewal in China could be summarized as micro-participants and micro-objects. Residents' cognition could be considered as “micro-renewal gene” in refer to meme theory. The application scenarios of introducing “micro-renewal gene” into the study of neighborhood renewal are of great potentialities.

Neighborhood micro-renewal system was constructed from the perspective of micro-participants and micro-objects. Moreover, neighborhood “micro-renewal gene” was proposed and applied into the study of this system in refer to meme theory.