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Titlebook: Data Mining and Bioinformatics; First International Mehmet M. Dalkilic,Sun Kim,Jiong Yang Conference proceedings 2006 Springer-Verlag Berl

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书目名称Data Mining and Bioinformatics
副标题First International
编辑Mehmet M. Dalkilic,Sun Kim,Jiong Yang
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Data Mining and Bioinformatics; First International  Mehmet M. Dalkilic,Sun Kim,Jiong Yang Conference proceedings 2006 Springer-Verlag Berl
描述This volume contains the papers presented at the inaugural workshop on Data Mining and Bioinformatics at the 32nd International Conference on Very Large Data Bases (VLDB). The purpose of this workshop was to begin bringing - gether researchersfrom database, data mining, and bioinformatics areas to help leverage respective successes in each to the others. We also hope to expose the richness, complexity, and challenges in this area that involves mining very large complex biological data that will only grow in size and complexity as geno- scale high-throughput techniques become more routine. The problems are s- ?ciently di?erent enough from traditional data mining problems (outside of life sciences) that novel approaches must be taken to data mine in this area. The workshop was held in Seoul, Korea, on September 11, 2006. We received 30 submissions in response to the call for papers. Each subm- sion was assigned to at least three members of the Program Committee. The Program Committee discussed the submission electronically, judging them on their importance, originality, clarity, relevance, and appropriateness to the - pected audience. The Program Committee selected 15 papers for pres
出版日期Conference proceedings 2006
关键词Alignment; Annotation; Microarray; Radiologieinformationssystem; algorithms; association rule mining; bioi
版次1
doihttps://doi.org/10.1007/11960669
isbn_softcover978-3-540-68970-6
isbn_ebook978-3-540-68971-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2006
The information of publication is updating

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Color Atlas of Burn Reconstructive Surgeryand performs prediction process for each fragment. A few computational models were implemented to detect signal patterns and their scanning efficiency was evaluated. Based on a few criteria, its prediction performance was compared with that of a few commonly used programs, GeneID and Morgan.
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Subspace Clustering of Microarray Data Based on Domain Transformation,nt on the number of genes. Based on the symbolic representations of genes, we present an efficient subspace clustering algorithm that is scalable to the number of dimensions. In addition, the running time can be drastically reduced by utilizing inverted index and pruning non-interesting subspaces. E
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Automatic Annotation of Protein Functional Class from Sparse and Imbalanced Data Sets,can be overcome with standard feature and instance selection methods. We also present a meta-learning scheme that utilizes multiple SVMs trained for each GO term, resulting in improved overall performance than either SVM can achieve alone. The implementation of the tool is available at http://fcg.ta
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