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Titlebook: Methods of Microarray Data Analysis IV; Jennifer S. Shoemaker,Simon M. Lin Book 2005 Springer-Verlag US 2005 Microarray.bioinformatics.car

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發(fā)表于 2025-3-25 05:10:09 | 只看該作者
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發(fā)表于 2025-3-25 10:11:10 | 只看該作者
Andrew V. Kossenkov,Ghislain Bidaut,Michael F. Ochsn one hour. The titre of the putative constitutive hsc 70 rises gradually after the wound healing stage and eventually increases to a level greater than controls during the late bud stage of regeneration. The results are discussed in light of a possible role for hsps and trauma induced proteins in t
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發(fā)表于 2025-3-25 11:40:48 | 只看該作者
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發(fā)表于 2025-3-25 18:38:39 | 只看該作者
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發(fā)表于 2025-3-25 20:34:52 | 只看該作者
Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetng cancer disease prediction and cancer therapy. The methodologies for integration and analysis of multiple gene expression data have been shown to perform well and could be generalized to broader applications.
26#
發(fā)表于 2025-3-26 01:13:57 | 只看該作者
Entropy and Survival-based Weights to Combine Affymetrix Array Types and Analyze Differential Expretecting important genes, we proposed a new method based on weighted t-tests (wtt). The survival information can be absorbed into a set of weights assigned to the expression intensities across all the arrays or subjects, based on the predicted median survival time using the Cox proportional hazards m
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發(fā)表于 2025-3-26 12:49:11 | 只看該作者
Microarray Data Analysis Using Neural Network Classifiers and Gene Selection Methods,n and optimized using Bayesian methods. The performance of the neural network was further enhanced using ensemble techniques such as bagging and boosting. The performance of all the resulting classifiers was compared using the Michigan and Harvard data sets from the CAMDA website. Three gene selecti
30#
發(fā)表于 2025-3-26 17:34:24 | 只看該作者
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