Intelligent coordinate expression gene analyzer

Abstract

The invention discloses an intelligent coordinate expression gene analyzer comprising a gene chip and a single chip computer. A gene expression profile of a biological sample is obtained through a gene chip collecting technique, and a gene set of coordinate expression is obtained by means of the single chip computer through an embedded analyzing technique. The single chip computer comprises a gene chip expression profile read module, a coordinate expression gene extraction module, a storing module and an output module. The extraction process of coordinate expression genes of the coordinate expression gene extraction module includes kernel function selection, immune clone memetic kernel biclustering algorithm coordination, obtaining of overlapped biclustering and the gene set of the coordinate expression. The intelligent coordinate expression gene analyzer can extract or express gene sets with a consistent trend or reversely expresses the related gene sets in the gene expression profile of the biological sample. Through searching for co-expression genes, not only are prompts provided for function researches of the genes, but also inspirations are provided for researches of gene regulation ways and gene regulation networks.

Description

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AI Technical Summary

Benefits of technology

This technology allows us to combine two different types of data into one more structure called a cluster or cube that improves its overall performance compared with existing methods like linear regression analysis alone. It also includes techniques used during training to improve accuracy over large datasets. Overall, this new approach helps identify patterns within complex biological systems faster than current approaches.

Problems solved by technology

Technologies aim towards finding ways to identify and isolate rare transcripts called target sequences within complex datasets containing many millions of nucleuses. Existing techniques involve sorting them manually without considering their structure characteristics. They require significant effort and take longer periods of time compared to combinatorial approaches due to the intrinsic dimensionality of the dataset itself. There is thus a technical challenge related to developing efficient tools capable of identifying and measuring unknown regions while minimizing computational resources required.

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