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首页> 外文会议>2017 International Conference on Intelligent Communication and Computational Techniques >Development of algorithm for gene expression analysis through MaxEnt based multivariate information theory: MaxEnt based algorithm for analyzing of gene expression
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Development of algorithm for gene expression analysis through MaxEnt based multivariate information theory: MaxEnt based algorithm for analyzing of gene expression

机译：通过基于MaxEnt的多元信息理论开发基因表达分析算法：基于MaxEnt的基因表达分析算法

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Human Genome Project generates a large amount of data. Simultaneously, it opens up Functional Genomics that ultimately results towards the development of Systems Biology. This subject area mostly relies on automated high-throughput technologies for data capture from biological systems. High-throughput technology generates a huge amount of data of multiple variables simultaneously. So handling and analysis of big data in this area becomes a challenge. For systems level functional annotations with designing of circuitry of the biological system, analysis of gene expression data holds the central position of Systems Biology research. To handle and analysis of these vast amount of data, generally different clustering and soft computing methodologies are already employed. Due to empirical nature of biological data Information Theoretic analysis has not been employed for many years in biological/medical problems, though it is well employed for physical systems. However, availability and application of automated techniques for biomedical data capture, recent time Information Theoretic approach is employed. This area of research can be accelerated further if some automated computational algorithm is available. To address this we have developed an algorithm for Multivariate Information Theoretic analysis. Output from our developed algorithm successfully tallies with the results available in literature.
机译：人类基因组计划会生成大量数据。同时，它开放了功能基因组学，最终导致了系统生物学的发展。该主题领域主要依靠自动化的高通量技术从生物系统中捕获数据。高通量技术可以同时生成大量具有多个变量的数据。因此，在这一领域中处理和分析大数据成为一个挑战。对于具有生物系统电路设计的系统级功能注释，基因表达数据的分析在系统生物学研究中占据中心位置。为了处理和分析这些大量数据，通常已经采用了不同的聚类和软计算方法。由于生物学数据的经验性质，尽管理论理论已经很好地用于物理系统，但在生物学/医学问题上尚未使用多年。然而，生物医学数据捕获的自动化技术的可用性和应用，最近采用了信息理论方法。如果可以使用某些自动计算算法，则可以进一步加速这一研究领域。为了解决这个问题，我们开发了一种用于多元信息理论分析的算法。我们开发的算法的输出成功地与文献中的结果相吻合。

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