Method for screening gene chip difference expression gene

Abstract

The invention provides a process of screening gene chip diversity expression gene, relating to an algorism for screening diversity expression gene in gene chip diversity expression analysis. The invention comprises following steps that firstly normalized processing chip data, secondly building up linear mold of logarithmic proportionality x ij= mu + mu j + epsilon, thirdly calculating globe mean value mu and the values of column effect mu j and variance sigma, fourthly calculating 2*ln (odd ratio) of each gene through utilizing mu, mu j and sigma, and fifthly setting localized value of X2 cutoff and n, and the gene of n is defined as diversity expression gene when the value of 2*ln (odd ratio) in the fourth step is larger than X2 cutoff. The invention designs appropriate statistic throughbuilding up statistical mode, and finally provides a significant probability numerical value for each gene as the standard of screening gene through utilizing the method of hypothesis test. The process overcomes the weaknesses that the conventional multiple process is lack of statistics basis and is difficult to assess the sensibility and specificity of the algorism self.

Description

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AI Technical Summary

Benefits of technology

In this new technology, we create an accurate mathematical equation called Statistics (statements) based on data collected from experiments or simulations. By doing these calculations, researchers can determine how well certain things are working with them compared against other ones without having any prior knowledge about it. These technical improvements make our methods more reliable than traditional techniques like counting numbers.

Problems solved by technology

This patented describes a technique called gene chip testing where different types of molecules like proteins or dyes attached to tiny sequences of nucleons are analyzed together at once. By comparing these patterns against their corresponding ones found within databases containing millions of similar templates, it becomes possible to identify differences between them indicating potential biological effects. These techniques have proven useful tools across diverse areas including medicine development, diagnostic purposes, and scientific study.

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