首页> 外文期刊>Journal of Bioinformatics and Computational Biology >NEW RESULTS IN BIOLOGICAL SEQUENCE ANALYSIS, COMPLEX GENE–DISEASE ASSOCIATION, qPCR CALCULATION, AND BIOLOGICAL TEXT MINING
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NEW RESULTS IN BIOLOGICAL SEQUENCE ANALYSIS, COMPLEX GENE–DISEASE ASSOCIATION, qPCR CALCULATION, AND BIOLOGICAL TEXT MINING

机译:生物序列分析,复杂基因-疾病关联,qPCR计算和生物文本挖掘的新结果

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This issue of the Journal of Bioinformatics and Computational Biology presentsneight papers on relevant problems such as biological sequences analysis, complexngene–disease associations, qPCR calculation, and biological text mining. Thesenpapers are briefly summarized below.nEffective as well as comprehensive detection and identification of functionalnfeatures in genomic and protein sequences has been an important direction in computationalnbiology.1 In this issue, Lam et al.2 and Johansson et al.3 present two differentnmethods for detecting interesting patterns in genomic and protein sequences,nrespectively. The former uses a graph mining algorithm to build hierarchical multilevelngraph structures — to take into consideration transposition, reversal, fusion,nfission, and translocation — to identify good candidate gene segments. The latternintroduces the relative von Neumann entropy for use in sequence analysis. Johanssonnet al.3 further show that this approach leads to better predictions of catalyticnsites compared to previously used entropies.
机译:本期《生物信息学与计算生物学杂志》就有关问题,如生物序列分析,复合基因-疾病关联,qPCR计算和生物文本挖掘,发表了八篇论文。有效,全面地检测和鉴定基因组和蛋白质序列中的功能特征已成为计算生物学的重要方向。1在本期中,Lam等人2和Johansson等人3提出了两种不同的检测方法基因组和蛋白质序列中的各种有趣模式。前者使用图挖掘算法来构建分层的多级图结构(要考虑转座,逆转,融合,分裂和易位),以识别良好的候选基因片段。后者介绍了用于序列分析的相对冯·诺依曼熵。 Johanssonnet等人[3]进一步表明,与以前使用的熵相比,这种方法可以更好地预测催化物。

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