Titlebook: Computational and Statistical Approaches to Genomics; Wei Zhang,Ilya Shmulevich Book 2006Latest edition Springer-Verlag US 2006 Microarray

Prognosis

頌揚(yáng)國家

https://doi.org/10.1007/b137323Microarray; Single Nucleotide Polymorphism; Termination; classification; complexity; data analysis; gene e

laparoscopy

敬禮

耕種

https://doi.org/10.1007/978-3-540-72128-4 issues. This is especially so in applications such as cancer classification where there is no prior knowledge concerning the vector-label distributions involved. It is clearly prudent to try to achieve classification using small numbers of genes and rules of low complexity (low VC dimension), and t

ATP861

Sampled-Data Control with Constant Delayuence of a protein may be considered as a twenty symbol alphabet sequence, or it may be considered as a sequence of numerical values reflecting various physicochemical aspects of the amino acids such as hydrophobicity, bulkiness, or electron-ion interaction potential. When primary protein sequence i

Inferior

鼓掌

Mirage

https://doi.org/10.1007/978-3-031-47835-2 networks. We have considered a learning strategy that is well suited for situations in which inconsistencies in observations are likely to occur. This strategy produces a Boolean network that makes as few misclassifications as possible and is a generalization of the well-known Consistency Problem.

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