By Eliot H. Ohlstein, Anthony G. Johnson, John D. Elliott (auth.), Richard S. Larson (eds.)
Comprises absolutely up-to-date protocols at the state of the art of the fields progress
Provides step by step element crucial fro reproducible results
Contains key notes and implementation suggestion from the specialists
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As can be seen, the first few eigenvalues are much larger than all of the other ones. In our experience, only three to five eigenvalues are significant and the others can be removed by truncation. A general rule of thumb is that when the eigenvalue is comparable to the error in the measured quantity in the data matrix, it should be truncated. For the case when E is truncated at two eigenvalues, designated Et, the inverse is given by 1 ε1 0 0 . . 1 0 ε2 0 . . = E−1 t (8) 0 0 0 ... in which all values in the matrix are zero except for the first two diagonal entries.
3. Frueh, F. , Hayashibara, K. , Brown, P. , and Whitlock, J. P. Jr. (2001) Use of cDNA microarrays to analyze dioxin-induced changes in human liver gene expression. Toxicol. Lett. 122, 189–203. 4. , Gonzales, F. , Jones, P. , Orntoft, T. , and Thykjaer, T. (2002) Analysis of gene induction in human fibroblasts and bladder cancer cells exposed to the methylation inhibitor 5-aza-2'-deoxycytidine. Cancer Res. 62, 961–966. 5. , Rushton, J. , Davis, L. , and Ness, S. A. (2004) Positive and negative determinants of target gene specificity in Myb transcription factors.
Nodes in the network can contain hyperlinks to outside databases, such as gene annotation databases, for easy exploration. Refer to the online documentations of these tools for details. 4. Clustering of Dynamic Parameters The elements of the Λ-matrix, λ ij, show the influence of the expression level of the jth gene on the production of the ith gene. Positive entries suggest a positive influence (either direct or indirect), and negative entries suggest an inhibition. We are interested in those genes that influence other genes strongly and identifying these by applying a threshold to the entries in the transition matrix.
Bioinformatics and Drug Discovery by Eliot H. Ohlstein, Anthony G. Johnson, John D. Elliott (auth.), Richard S. Larson (eds.)