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Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2006: E Franz Rothlauf,Jürgen Branke,Hideyuki Takagi Conference proceedings 2006 Sprin

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樓主: infection
41#
發(fā)表于 2025-3-28 17:35:12 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/159412.jpg
42#
發(fā)表于 2025-3-28 20:45:03 | 只看該作者
https://doi.org/10.1007/978-0-387-30424-3PCR sequences are known, many of them remain as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-l
43#
發(fā)表于 2025-3-29 00:28:03 | 只看該作者
Encyclopedia of Language and Educationated to the GO. A knowledge-driven cluster validity assessment system for microarray data was implemented. Different methods were applied to measure similarity between yeast genes products based on the GO. This research proposes two methods for calculating cluster validity indices using GO-driven si
44#
發(fā)表于 2025-3-29 04:04:20 | 只看該作者
Encyclopedia of Language and Educationllow us to analyze the sequences and functions of different genes simultaneously and detect small differences in those. They are source of tremendous amount of data in the field of Bioinformatics. Moreover, the engineering process of DNA chip requires the latest results of information technology, to
45#
發(fā)表于 2025-3-29 10:19:48 | 只看該作者
46#
發(fā)表于 2025-3-29 12:02:41 | 只看該作者
47#
發(fā)表于 2025-3-29 16:23:26 | 只看該作者
Encyclopedia of Language and Educationcording to its malignant potential, and detection of the risk type is important to understand the mechanisms and diagnose potential patients. In this paper, we classify the HPV protein sequences by support vector machines. A string kernel is introduced to discriminate HPV protein sequences. The kern
48#
發(fā)表于 2025-3-29 21:04:19 | 只看該作者
49#
發(fā)表于 2025-3-30 03:21:06 | 只看該作者
Adventism, Eight Laws of Healthstudy of disease activity. To this aim, feature selection techniques based on machine learning can be applied for detecting potential biomarkes and biomaker patterns. A key issue concerns the interpretability and robustness of the output results given by such techniques. In this paper we propose a r
50#
發(fā)表于 2025-3-30 04:10:38 | 只看該作者
https://doi.org/10.1007/978-1-4614-7753-2e approach is based on the use of a new information theoretic selection criterion: the double input symmetrical relevance (DISR). The rationale of the criterion is that a set of variables can return an information on the output class that is higher than the sum of the informations of each variable t
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