Inference on Missing Values in Genetic Networks Using High-Throughput Data
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High-throughput techniques investigating for example protein-protein or protein-ligand interactions produce vast quantity of data, which can conveniently be represented in form of matrices and can as a whole be regarded as knowledge networks. Such large networks can inherently contain more information on the system under study than is explicit from the data itself. Two different algorithms have previously been developed for economical and social problems to extract such hidden information. Based on three different examples from the field of proteomics and genetic networks, we demonstrate the great potential of applying these algorithms to a variety of biological problems.
KeywordsPositive Eigenvalue Genetic Network Knowledge Network Physical Review Letter Cytokine Network
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- 3.Frankenstein, Z., Alon, U., Cohen, I.: The immune-body cytokine network defines a social architecture of cell interactions. Biology Direct 1(32), 1–15 (2006)Google Scholar
- 5.Liò: Dimensionality and dependence problems in statistical genomics. Brief Bioinform 4, 168–177 (2003)Google Scholar
- 7.Maslov, S., Zhang, Y.-C.: Extracting Hidden Information from Knowledge Networks. Physical Review Letters 87(24), 248701_1–248701_4 (2001)Google Scholar
- 8.Porto, M., Bastolla, U., Roman, H.E., Vendruscolo, M.: Reconstruction of Protein Structures from a Vectorial Representation. Physical Review Letters 92(21), 218101_1–218101_4 (2004)Google Scholar
- 9.Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., Futcher, B.: Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273–3297 (1998)Google Scholar