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首页> 外文期刊>International journal of computational models and algorithms in medicine. >Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances
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Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances

机译：生物信息学和计算系统生物学的系统方法：最新进展

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Bioinformatics is an interdisciplinary field that combines Molecular Biology and Computer Science, and focuses in the management of the information contained in biological macromolecules (DNA molecules, genes and proteins) using computational techniques from the areas of data structures, machine learning, data bases, information retrieval and computational geometry. Computational Systems Biology, deals with modeling complex interactions between biological systems, employing a holistic approach, in order to understand how the various properties of a biological system as a whole, emerge. Thisspecific volume is comprehensive in quality and quantity study concerning systemic approaches in the areas of Bioinformatics and Computational Systems Biology. The volume contains a series of articles that explain algorithmic approaches that emerge when developing systemic approaches; the articles cover a broad range of topics that could benefit both theoretical insight and practical applications in the Bioinformatics' community. In particular the book consists of eleven chapters each exploiting a different theme of the specific research area. The first chapter is written by Miroslava Cuperlovic-Culf and discusses unsupervised data analysis methods employed in Metabo-lomics and Metabonomics. A survey concerning the pros and cons of various clustering techniques and data analysis methods such as PCA (Principal Component Analysis), HCL (Hierarchical Clustering), KM (k-means), SOM (Self Organized Maps) and FKM (Fuzzy k-means) is presented. The survey is both theoretical and practical, with experiments on a large set of carefully produced synthetic and real data, and interesting conclusions concerning the functionality of the various algorithms are assembled. The main conclusion drawn is the usefulness of PCA when combined with other techniques, the superiority of bagged k-means in quantified data when compared to k-means, the excellence in performance of SOM, and the high efficiency of Fuzzy k-means; these three methods should be explored in metabolic applications.
机译：生物信息学是一个将分子生物学和计算机科学相结合的跨学科领域，致力于利用数据结构，机器学习，数据库，信息等领域的计算技术，对生物大分子（DNA分子，基因和蛋白质）中包含的信息进行管理。检索和计算几何。计算系统生物学致力于采用整体方法来对生物系统之间的复杂相互作用进行建模，以了解生物系统整体的各种特性是如何出现的。在生物信息学和计算系统生物学领域，有关系统方法的质量和数量研究涵盖了这个特定的内容。该卷包含一系列文章，这些文章解释了开发系统方法时出现的算法方法。这些文章涵盖了广泛的主题，这些主题可能有益于生物信息学界的理论见解和实际应用。特别是，该书由11章组成，每章都利用特定研究领域的不同主题。第一章由Miroslava Cuperlovic-Culf撰写，讨论了代谢组学和代谢组学中使用的无监督数据分析方法。关于各种聚类技术和数据分析方法的利弊的调查，例如PCA（主成分分析），HCL（分层聚类），KM（k均值），SOM（自组织图）和FKM（模糊k均值） ） 被表达。这项调查是理论上的，也是实际的，对大量精心制作的合成和真实数据进行了实验，并得出了有关各种算法功能的有趣结论。得出的主要结论是PCA与其他技术结合使用的有效性，袋装k均值与k均值相比在量化数据中的优越性，SOM的卓越性能以及Fuzzy k均值的高效率;在代谢应用中应探索这三种方法。

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