Currently DNA micro-array experiments have been done extensively in genomic research and analyzing those data is a challenging problem. Here we would like to suggest a representation method for the data of a single gene expression over a certain period. This is the summary of the paper in full text, available in www.biofront.biz or http://arxiv.org/abs/cs.CC/0305008 . Note: DNA micro-array techniques convert the expression rates into densities of stained images, which may be recorded as a series of numbers. All the experiments and phenomenon infer that the numbers fluctuate over the time. Step 1. Function representation fitting with data Since we need a periodic-looking, fluctuating functions, it will be wise to start with sin(t) or cos(t), while, for increasing and decreasing effects, the exponential function would be the feasible choice. Consequently, a possible function for representing the changes of the expression rate would be of the form exp(kt)sin(mt) or exp(kt)cos(mt), where k and m are real constants. The exponential functions have their shares in science, especially, in modeling problems and theories, so it is not surprising that exponential functions make their appearances here. Although we are comfortable and familiar with the function, once in a while, one might ask the following question: why do the exponential functions appear so often? Although the answer to this question is not obvious, I would like to justify my choice of the exponential function here. The clue could be in the profound experimental fact, i.e., that the radioactive decay is measured in terms of half-life ? the number of years required for half of the atoms in a sample of radioactive material to decay. Mathematically this is expressed as y' = ky Here y represents the mass and k is the rate constant. Then the general type of a solution looks like y = Cexp(kt), where t represents time and C is a constant. This might be extended to observe some sort of life expectancy of a certain phenomenon or behavior. Step 2. Determining coefficients of the functional representation Once we fix a candidate function, it remains to determine the coefficients C ¡¯s and k¡¯s etc., for each set of data. This may be achieved by using the least square sum principle with high accuracy set to our own standard. Commercial software, such SAS and SPSS, are available for such calculation, namely, R-squared. The least square sum method, as the most popular one for fitting a curve/function with experimental data, finds the coefficients of a function of given type, by minimizing the sum of square of errors, or deviations. More precisely, given a set of data points, (x1, y1), (x2, y2)... (xn, yn) and a candidate function f with undetermined coefficients, the unknowns in f would be determined so that the summation of squares of difference of errors, f(x) and y, be minimized. Note that two is the smallest and good for further manipulation, i.e., we could use many tools, calculus, involving differentiation unlike the absolute value function, | |. Step 3. Vector representation for machine learning method From the first two steps, we have obtained a ¡°functional¡± representation for observed data, i.e., a function fitting with the data. Consequently, with respect to the fixed type, each function is represented as a set of coefficients calculated in the step two. Suppose the data fits well with model, y = Cexp(kt)sin(mt), where y is the expression rate. Then we can say that the set of numbers (C, k, m) represent the object, A. In other words, the object may be identified with the triple (C, k, m), analogous to students and their corresponding ID numbers. For the general case, i.e., the gene expression rates of n multiple genes, we will get (C1, k1, m1), (C2, k2, m2),.., (Cn, kn, mn), which form a vector. Conclusion: Once we have the vector representation, by using SVMs, we would get a criterion, which may be applied for diagnosis of a disease etc.