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Human genome is a multifaceted complexity of system and functions within the human body. Human genome is specifically all of the genetic information of the entire human race. The human genome encodes information that instructs human development, physiology, medicine, and evolution. It is understanding the thousands of rare, inherited diseases and as a way to speed the harder task of pulling apart the role of genes and environment in common diseases, such as cancer, diabetes, and Alzheimer’s, etc. As such, the human genome explains most of the phenotypical differences between people and carries the actual blueprints for proteins, the ensemble of “expressed” DNA sequences, or exons (Venter, et. al., 2001).
Genome sequences provide a conceptual framework within which much future research in biology will be structured. Genome sequences are important for addressing questions concerning evolutionary biology and understanding human evolution, the causation of disease, and the interplay between the environment and heredity in defining the human condition. Therefore, whole genome sequencing (WGS) allows researchers to pinpoint genetic differences between individuals and significantly shortcuts the costly and time-consuming part of forward genetic analysis in model organism systems. In 1977, Sanger’s studies of insulin first demonstrated the importance of sequence in biological large-scale molecules. By 1987, two genes were successfully sequenced using the first automated DNA sequencer, developed by Applied Biosystems in California. And in 1993, the Institute for Genomic Research (TIGR), developed that analyzed hundreds of thousands of expressed sequenced tag (ESTs). These studies championed the development of early sequencing of human genomes to annotate and validate gene predictions in the human genome (Venter, et. al., 2001).
Genome-wide association studies (GWAS) identifies genetic variants associated with numerous human traits and common diseases. Genome-wide association studies (GWAS) are providing a powerful approach to identifying common disease loci, and the GWAS catalog currently contains ~17,600 loci where variants have been found to be associated with the phenotypes of ~1100 human complex traits ( Genome-wide association studies validates genetic influence on height; glucose, cholesterol, and blood pressure levels; and risks for childhood-onset and adult-onset diabetes, macular degeneration of the retina, various cancers, coronary heart disease, mental illnesses, inflammatory bowel disease, and other diseases. In each of these loci, genetic variants affect the function of one or more gene products. The large number of recombination events in the genetic history of the population, only markers in tight linkage disequilibrium with loci responsible for the trait variation will exhibit significant statistical association with the trait.
Genomic sequencing in CDSS, and the rapid development of novel, faster sequencing technologies is making possible the era of personalized human genomics and will contributed to our better understanding of variation in the human genome (Stephens, et. al., 1990). However, incidental or secondary findings, which can occur in large numbers can be potential barrier to the utility of this new technology due to their relatively high prevalence and the lack of evidence or guidelines available to guide their clinical interpretation. A decade from now, CDSS with definitions, classifications that focuses on the clinical aspects of handling incidental findings, and determining their clinical relevance and utility can translate into medically useful information which will prove beneficial and will open up a new appreciation towards the vastness of individual genetic variations. This deeper understanding of the biology of our genome are necessary in order to decipher, interpret, and optimize clinical utility of what the difference in the human genome can teach us. Personal genome sequencing may eventually become an instrument of common medical practice, providing information that assists in the formulation of a differential diagnosis.
Boolean logic offer a more structured approach to the construction of empirical typologies. This feature converges with the purpose of empirical typology: to provide a useful shorthand for describing the diversity that exists within a given class of social phenomena. These techniques can be used to compare clusters holistically and to identify their key underlying differences. The Boolean approach provides explicit, logical rules for simplifying complexity. This process is conducted in a bottom-up fashion until no further stepwise reduction of Boolean expressions is possible. It is bottom-up that is, inductively oriented and seeks to identify ever wider sets of conditions for which an outcome is true. Questions in Boolean logic terms are structured as (“or,” “and”), selected from relevant databases, and searched using terms to locate the best evidence. Boolean logic, aims to identify the necessary and sufficient conditions across cases required to produce a particular outcome. It attempts to synthesize all cases of a phenomenon (e.g. diabetes) into a dataset so that all cases with the same outcome (e.g. signs of slow-healing wounds, cuts, or sores) that can be explained using Boolean logic. The method uses what is called a ‘truth table’ to lay out all logically possible combinations of the presence or absence of independent (explanatory) variables related to a particular outcome variable. Data are coded into binary format (0 or 1) according to whether the variable is present or not.
P = Is patient showing signs of diabetes
R= Blurred vision
Q = signs of slow-healing wounds, cuts, or sores
R = Not(P) And Q

Logistic regression is an analysis which enables us to estimate categorical results like group membership with the help of a group of variables. Logistic regression analysis, does not require assumptions to meet concerning the distribution of independent variables and independent variables could be constant or categorical. In other words, assumptions such as normal distribution of independent variables, linearity and equality of variance-covariance matrix do not have to be met. In addition, the candidate predictor variables can be continuous, discrete, and/ or dichotomous. Logistic regression has the potential to be a powerful tool in predicting dichotomous outcomes. A dichotomous outcome is one that can be coded into one of two mutually exclusive categories, e.g., patients with positive results as compared to those patients with false-positive results. Therefore, it might be suggested that logistic regression analysis is much more flexible. On the other hand, in logistic regression, the estimated value ranges from 0 to 1. More clearly, logistic regression reveals the possibility of particular consequences for each subject and from this analysis produces a regression equation which enables us to make an accurate estimation for the possibility that an individual falls into one of the above-mentioned categories. As a result, the possibility of occurrence of one of the values which the dependent variable might have is estimated in logistic regression analysis.
So, in CDSS, logistic regression allows the measurement of the association between the occurrence of an event (diabetes) and factors susceptible to influence it (e.g. signs of slow-healing wounds, cuts, or sores). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. In statistical testing, the item responses to a test are usually transformed into a total score that is distributed within a range of values (e.g., from 0 to 50). In some decision making situations, one cut-off score must be established on the total test score in order to classify individuals into two categories (e.g., whether diabetes disorder is present or absent). In clinical settings, this type of problem is frequently solved with the construction of Receiver Operating Characteristic (ROC) curves. ROC curves are used to summarize diagnostic performance of a test by plotting true-positive rate (sensitivity) versus false-positive rate (1- specificity) for each possible cut-off score (threshold). From ROC analysis, a cut-off score on a test can be established taking into account factors such as disorder prevalence (or base rate), and the cost-benefit relation of the various decisions.
A variety of decision support systems has been developed to assist general physician in making decisions. For example:
• Care provider order entry system (CPOE)
• Clinical guidelines
• Condition-specific order sets
• Focused patient data reports and summaries
• Documentation templates
• Diagnostic support
Clinical guidelines provides a set of recommendations for the best standards of care, based on the findings of evidence-based medicine.

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