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5 results on '"Shustova, Valentina"'

2. Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions

Author

Selaru, Florin M., primary, Xu, Yan, additional, Yin, Jing, additional, Zou, Tong, additional, Liu, Thomas C., additional, Mori, Yuriko, additional, Abraham, John M., additional, Sato, Fumiaki, additional, Wang, Suna, additional, Twigg, Charlie, additional, Olaru, Andreea, additional, Shustova, Valentina, additional, Leytin, Anatoly, additional, Hytiroglou, Prodromos, additional, Shibata, David, additional, Harpaz, Noam, additional, and Meltzer, Stephen J., additional

Published
2002
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4. Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon.

Author

Tong-Tong Zou, Selaru, Florin M., Yan Xu, Shustova, Valentina, Jing Yin, Mori, Yuriko, Shibata, David, Sato, Fumiaki, Suma Wang, Olaru, Andreea, Deacu, Elena, Liu, Thomas C., Abraham, John M., and Meltzer, Stephen J.

Subjects
COLON cancer, DNA microarrays, PRECANCEROUS conditions, EPITHELIAL tumors, COMPUTER software
Abstract

Presents a study where microarrays were hybridized to labeled RNA obtained from several colonic specimens in order to discover global gene expression patterns characterizing subgroups of colon cancer. Information on colorectal cancer; Importance of the early detection of colon cancer in the premalignant stages; Application of the software program Significant Analysis of Microarrays to identify genes whose expression were used to distinguish between normal and cancerous colonic epithelium.

Published
2002
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5. Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer.

Author

Xu Y, Selaru FM, Yin J, Zou TT, Shustova V, Mori Y, Sato F, Liu TC, Olaru A, Wang S, Kimos MC, Perry K, Desai K, Greenwald BD, Krasna MJ, Shibata D, Abraham JM, and Meltzer SJ

Subjects
Adenocarcinoma genetics, Adenocarcinoma metabolism, Barrett Esophagus metabolism, Carcinoma, Squamous Cell genetics, Carcinoma, Squamous Cell metabolism, Cluster Analysis, Computational Biology methods, Esophageal Neoplasms metabolism, Gene Expression Profiling, Humans, Multigene Family, Oligonucleotide Array Sequence Analysis, Barrett Esophagus genetics, Esophageal Neoplasms genetics, Neural Networks, Computer
Abstract

cDNAmicroarrays, combined with bioinformatics analyses, are becomingincreasingly used in current medical research. Existing analytic methods,particularly those that are unsupervised, often have difficulty recognizing subtle differences among predefined subgroups. In contrast, supervised methods, such as Artificial Neural Networks (ANNs), are able to recognize subtly different biological entities. We applied ANNs in a proof-of-principle study of cDNA microarray data in esophageal cancer (CA) and premalignancy. cDNA microarrays, each containing 8064 clones, were hybridized to RNAs from 22 esophageal lesions, including 14 Barrett's esophagus (BA) metaplasias and 8 esophageal carcinomas (3 squamous cell carcinomas and 5 adenocarcinomas). Scanned cDNA microarray data were analyzed using the bioinformatics software Cluster/TreeView, Significance Analysis of Microarrays (SAM), and ANNs. Cluster analysis based on all 8064 clones on the microarrays was unable to correctly distinguish BA specimens from CA specimens. SAM then selected 160 differentially expressed genes between Barrett's and cancer. Cluster analysis based on this reduced set still misclassified 2 Barrett's as cancers. The ANN was trained on 12 samples and tested against the remaining 10 samples. Using the 160 selected genes, the ANN correctly diagnosed all 10 samples in the test set. Finally, the 160 genes selected by SAM may merit further study as biomarkers of neoplastic progression in the esophagus, as well as in elucidating pathological mechanisms underlying BA and CA.

Published
2002

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