Tobacco smoking is a widespread behavior, and is considered one of the major preventable causes of ill health and death. The direct link between tobacco smoking and prevalence of major diseases is well established, which will continue to pose a significant threat to human health. It has been well-documented that smoking substantially increases the risk of heart disease, and accounts for about one-third cancer deaths. About 90 percent of all deaths from chronic obstructive pulmonary diseases are attributable to cigarette smoking. It is linked to vision loss through cataracts, macular degeneration, and other eye health disorders. The risk is not limited to, tobacco smokers, it extends to nonsmokers especially children, who involuntarily inhale environmental tobacco smoke (ETS) at home, at work, or in public places. Bioinformatics and statistics methods have important applications in multidisciplinary research. One of vital application is to uncover a correlation between genetic and environmental factors in tobacco smoking addiction for better understanding of risk assessment, improving diagnosis, prevention and treatment. Statistical studies can correlate socioeconomic status of smokers in presumed normal individuals and patients. In addition, Bioinformatics can show the genomic context of novel genes connected with tobacco smoking behavior.
Tobacco smoking is a widespread behavior, and is considered one of the major preventable causes of ill health and death. The direct link between tobacco smoking and prevalence of major diseases is well established, which will continue to pose a significant threat to human health. It has been well-documented that smoking substantially increases the risk of heart disease, and accounts for about one-third cancer deaths. About 90 percent of all deaths from chronic obstructive pulmonary diseases are attributable to cigarette smoking. It is linked to vision loss through cataracts, macular degeneration, and other eye health disorders. The risk is not limited to, tobacco smokers, it extends to nonsmokers especially children, who involuntarily inhale environmental tobacco smoke (ETS) at home, at work, or in public places. Bioinformatics and statistics methods have important applications in multidisciplinary research. One of vital application is to uncover a correlation between genetic and environmental factors in tobacco smoking addiction for better understanding of risk assessment, improving diagnosis, prevention and treatment. Statistical studies can correlate socioeconomic status of smokers in presumed normal individuals and patients. In addition, Bioinformatics can show the genomic context of novel genes connected with tobacco smoking behavior.
