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Bioinformatics and Biomedicine Workshops

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This is a technique that is used in the conducting of very highly sensitive experiments that are biological in nature such as in detection of DNA, or in micro array experiments.  The micro array technique is a very sensitive analytical procedure that can be used to analyze multiple illnesses. The only problems with this experimental techniques is that they are expensive, involve a very large number of samples which are complex and very difficult to analyze. These genes are the ones that are referred to as the biomarkers. These genes are identified in studies through the micro array technique. The procedure that leads to the identification of these genes is by a process of continuous characterization of cells especially in the presence of cancer. This has been used in the enhancement of the disease diagnosis and prognosis potential.

The micro array technique has the following shortcomings, the number of the genes is highly dimensional and they indicate and show a scarce number of replicates. This study is highly variable across the replicates, the data that is found in micro array technique is never homogeneous and therefore requires normalizing and most of the data is normally non -linearly. This makes it very hard to study using the micro array technique.

In order for this to be overcome, the data will have to following strategy: First; the microarray experiment will have to be done at two levels, in a healthy individual and in a patient with characteristic of cancer. Secondly, each gene is to be represented with multiple performance measures where the genes are represented with a value usually a value (P). Thirdly, a statistical data envelopment strategy is used. This finds the complex envelop of particular data set consistently. This eliminates the need for the manual manipulation of information. This is an application of linear programming where linearity is an important application. Next, the genes are selected in a series of frontiers, in this techniques, several layer of genes are selected containing a variable number of genes. This is found through the variation of the p-value. The candidates selected in this mode are named as efficient genes. The other step will involve the generation of an experimentation design that will vary the efficient genes, this is used to dictate the value of interest between the values zero (0) and (1). At every step, the performance value is determined is determined with a linear discriminant analysis which sets the classification performance.  This is then fitted in a first order linear regression model; this expresses the direct relation between the performance values to the efficient genes which is set as an independent variable. Finally, the integer linear regression programming is applied to help make a choice of the potential biomarker that will be able to maximize the classification performance for the diagnosis of the specified disease condition. This is the process that is referred the as the consistent detection of potential biomarkers with linear models.

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