Fuzzy method of recognition of high molecular substances in evidence-based biology

Nowadays modern requirements to achieving reliable results along with high quality of researches put mathematical analysis methods of results at the forefront. Because of this, evidence-based methods of processing experimental data have become increasingly popular in the biological sciences and medicine. Their basis is meta-analysis, a method of quantitative generalization of a large number of randomized trails contributing to a same special problem, which are often contradictory and performed by different authors. It allows identifying the most important trends and quantitative indicators of the data, verification of advanced hypotheses and discovering new effects in the population genotype. The existing methods of recognizing high molecular substances by gel electrophoresis of proteins under denaturing conditions are based on approximate methods for comparing the contrast of electrophoregrams with a standard solution of known substances. We propose a fuzzy method for modeling experimental data to increase the accuracy and validity of the findings of the detection of new proteins.