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Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 6th European Confere Elena Marchiori,Jason H. Moore Conferenc

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发表于 2025-3-21 20:08:43 | 显示全部楼层 |阅读模式
书目名称Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
副标题6th European Confere
编辑Elena Marchiori,Jason H. Moore
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 6th European Confere Elena Marchiori,Jason H. Moore Conferenc
出版日期Conference proceedings 2008
关键词Microarray; bioinformatics; biology; data mining; evolution; evolutionary computation; genetics; learning; m
版次1
doihttps://doi.org/10.1007/978-3-540-78757-0
isbn_softcover978-3-540-78756-3
isbn_ebook978-3-540-78757-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Jeanne Peijnenburg,David Atkinsonin both family-based and case-control datasets. In this manuscript, we demonstrate how LD patterns of the simulated data change under different population growth curve parameter initialization settings. These results provide guidelines to simulate WGA datasets whose properties resemble the HapMap.
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Mining Gene Expression Patterns for the Discovery of Overlapping Clusters, both a local pairwise similarity measure between gene expression profiles and a global probabilistic measure of interestingness of hidden patterns. When performing re-clustering, the proposed approach is able to distinguish between relevant and irrelevant expression data. In addition, it is able to
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Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysisal researchers. The goal of the present study was to develop and evaluate a prototype CE system for the analysis of human genetics data. We describe here this new open-ended CE system and provide initial results from a simulation study that suggests more complex operators result in better solutions.
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