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WIREs Nanomed Nanobiotechnol
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Nanogenomics in medicine

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Abstract This review presents the status of technological developments of nanogenomics and its applications to medicine. Even if particular emphasis is placed on what has been accomplished in our laboratory in the last few years in the area of genes microarrays, significant reference to the recent activity of numerous other groups can be found in Refs 1,2 WIREs Nanomed Nanobiotechnol 2010 2 59–76 This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology

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Nanogenomic diagnostics.

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Gene–APA and protein–APA linkage via Poly‐L‐Lysine (right) and FIB system images of cross‐sectional morphologies of the microarray spot, resulting at the end of photolithographic microstructuring technique and 2‐step anodization process (left).

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Interaction network for genes distinguishing lymphoma from normal T cells. Subnetwork connecting the four leader genes which are “neutral” according to their expression pattern are shown with a dotted line.

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Classification probabilities of individual patients by predictive analysis of microarray (PAM) based on the 343 differentially expressed genes between operationally tolerant kidney graft recipients (TOL) and patients with chronic rejection (CR). Each patient sample is shown by a bar, as labeled in the X‐axis. The color codes indicate the probability (0–1, as indicated in Y‐axis) that the sample belongs to TOL (red) or CR (green). (a) Cross‐validated probabilities on the 8 TOL and 18 CR that were used to set up the 2‐class (TOL/CR) classification algorithm. Among the 26 patients, 5 samples (CR013, CR014, TOL01, TOL06, and TOL08) were misclassified. (b) Using the PAM algorithm defined with 8 TOL and 18 CR patients, 7 serially harvested samples (4 TOL and 3 CR) at a time interval of more than 1 year after the first sample were classified. The algorithm correctly classified all samples, with a probability of 100% for TOL and 92.0% for CR. (Reprinted with permission from Ref 35. © 2008, Wiley‐Liss, Inc., a subsidiary of John Wiley & Sons, Inc).

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Complete interaction map “no textmining” interaction map calculated for the “Class 1” reliable genes and filtered by (CR‐TOL) amplitude, as obtained from the new fullchip microarray datasets (see Ref 7, for their names and ranking according to their number of interactions). (Reprinted with the permission from Ref 7 © 2008, Wiley‐Liss, Inc., a subsidiary of John Wiley & Sons, Inc).

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Genes from the “fullchip” plotted according to their tolerance or rejection propensity, i.e., difference in expression in log scale between genes from operationally tolerant kidney graft recipients (TOL) and genes from patients with chronic rejection (CR), with the 56 SAM‐identified genes marked. Arrows indicate genes included in the 56 gene dataset but possibly unable to discriminate rejection/tolerance. Lines indicate thresholds used in selecting genes for “pro‐tolerance” and “pro‐rejection” leader gene calculations. (Reprinted with the permission from Ref 7. © 2008, Wiley‐Liss, Inc., a subsidiary of John Wiley & Sons, Inc).

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Final map of interactions among eight high‐ranking genes in cell cycle of human T lymphocytes and their neighbors. (Reprinted with the permission from Ref 24. © 2006, Wiley‐Liss, Inc., a subsidiary of John Wiley & Sons, Inc).

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Top—percentages of classes of genes belonging to the four categories with respect to reliabilities in the old “fullchip” raw dataset. Bottom—the same values (green) compared to fractions (%) of genes not adhering to the HGNC nomenclature (“bad”) in each genes with respect to total “bad” genes. Left—CR data. Right—TOL data. CR dataset consists of 42 samples in total and 6864 genes, while TOL dataset consists 28 samples in total and 6864 genes. Reliability is given by the percentage of proven expression data by GENEPIX in such genes for 70 microarray samples. (Reprinted with the permission from Ref 7. © 2008, Wiley‐Liss, Inc., a subsidiary of John Wiley & Sons, Inc).

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Relations between the genes induced during lymphocyte activation according to gene–gene interaction (induction or suppression). (Reprinted with the permission from Ref 10. © 2006, Wiley‐Liss Inc., a subsidiary of John Wiley & Sons, Inc).

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