神经网络的BP训练算法和遗传优化训练算法的对比研究

BP算法是人工神经网络的传统常用训练算法。遗传算法是一种新型的、随机性的、全局性的优化方法。基于MATLAB对比这两种训练方法的异同和优缺点,从而达到神经网络的最优化训练,充分发挥神经网络的作用。     

Seymour Benzer and T4 <Emphasis Type="Italic">r</Emphasis>II

One of the goals of genetics is to understand genes in as much detail as possible. For instance, with respect to a given gene, one would like to know its chromosomal location, its physical and genetic size, its neighbours, the number of mutations/alleles defining the gene, the order of mutations, the genetic/physical distance between them, etc. Thus, rather than focusing on the whole genome, one focuses on the finer details of a given genetic segment. The exercise of probing such details is called ‘fine structure genetic analysis’. There are several pioneers who have contributed enormously to this area in many bacterial and phage systems. Two stalwarts, Seymour Benzer and Charles Yanofsky, stand preeminent among them. In the following pages I present briefly Benzer’s outstanding work on the fine structure of the rII region of bacteriopage T4. These path-breaking studies contributed significantly to our understanding of the structure, organization and function of genes. R Jayaraman is an Emeritus Professor of Molecular Biology at the School of Biological Sciences, Madurai Kamaraj University, Madurai, where he taught and carried out research in the area of microbial genetics for three decades.     

运动基因组学的研究进展

目的:回顾目前在理解运动员能力遗传决定论方面的进展,并描述一些新的、重要的、可能成为精英运动员的差异标记的基础的DNA多态性。方法:通过文献综述和归纳分析法对过去几十年运动基因组学的研究进展作了简单的回顾。结果:运用全基因组关联研究(GWAS),在过去几年确定了41种标志物,这表明GWASs是一种有前景、富有成效的研究运动相关表型的方法。结论:未来的研究,包括多中心GWAS和大群运动员的全基因组测序的验证和复制研究,将有助于发现大量能部分解释运动能力和相关表型的遗传力的遗传变异(突变和DNA多态性)。