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| Procedure for Neural Network Analysis of Microarray Data |
| Chips with Specific Application to the Diagnosis and |
| Prognosis of Lymphoma Patients |
| Neural networks are particularly well suited to drawing summary conclusions |
| from large-scale (1000's of genes) microarray experiments in that they are |
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| tasked to adjust the weights on each input neuron (gene) until they can |
| correctly classify the entire training set. These networks, once trained, can |
| then be numerically differentiated to provide specific information about the |
| relative contribution of particular genes in a given context; this offers a more |
| specific alternative to the commonly used statistical grouping methods. |
| The identification of specific genes associated with a particular biological |
| characteristic such as malignant phenotype would be useful in many |
| settings, for example: 1) T cell and antibody-mediated immunotherapy may |
| be efficacious approaches for limiting tumor growth in cancer patients. At |
| present there is a paucity of known tumor rejection antigens that can be |
| targeted. Neural net analysis may identify a panel of tumor encoded genes |
| shared by many patients with the same type of cancer and thereby provide |
| a repertoire of potentially novel tumor rejection antigens. 2) Precise |
| classification and staging of tumors is critical for the selection of the |
| appropriate therapy. At present, classification is accomplished by |
| morphologic, immunohistochemical, and limited biological analyses. Neural |
| net analysis in the form of specific donor profiles could provide a fine |
| structure analysis of tumors characterizing them by a precise weighting of |
| the genes which they express differentially. Neural net profiling may identify |
| gene panels which are stage specific. 3) At present, only subsets of patients |
| with a given type of tumor respond to therapy. Networks trained to |
| distinguish responders from non-responders would allow a comparison of |
| tumor-expressed genes in responders and non-responders to find those |
| genes most predictive of response. Given the significant impairment in the |
| quality of life for many patients undergoing chemotherapy and/or radiation |
| therapy, such prospective information would be extremely beneficial. 4) For |
| many patients with autoimmune disease the target antigen(s) is unknown. |
| Enhanced identification of cell-type specific markers of the target organ |
| through neural net profiling could identify potential target antigens as |
| candidate molecules for testing and tolerance induction. |
| We believe neural networks will be an ideal tool to assimilate the vast |
| amount of information contained in microarrays. Indeed, the trained neural |
| network may, in the form of its weight matrix, have the best possible take on |
| the very broad statement being made in the microarray, a view which is |
| accessible with the differentiation of the network. In this study, that viewpoint |
| suggested a small subset of genes which proved sufficient to give a near- |
| perfect classification. This approach should be suitable for any microarray |
| study which contains sufficient training data. |
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