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New PDF release: Artificial Neural Networks in Cancer Diagnosis, Prognosis,

By R. N. G. Naguib, G. V. Sherbet

ISBN-10: 0849396921

ISBN-13: 9780849396922

The capability price of synthetic neural networks (ANN) as a predictor of malignancy has began to obtain elevated attractiveness. learn and case experiences are available scattered all through a mess of journals. man made Neural Networks in melanoma prognosis, analysis, and sufferer administration brings jointly the paintings of most sensible researchers - essentially clinicians - who current the result of their cutting-edge paintings with ANNs as utilized to almost all significant components of melanoma for prognosis, analysis, and administration of the disease.The e-book introduces the speculation of neural networks and the strategy in their software in oncology. it isn't an workout in ANN study, however the presentation of a brand new procedure for diagnosing and making a choice on the therapy of cancers. The authors have integrated just about all cancers for which there exist ANN purposes. whilst the knowledge to be had is ill-defined and the advance of an algorithmic answer tricky, neural networks supply a non-linear technique which is helping sift throughout the maze of knowledge and arrive at an inexpensive solution.Highly interdisciplinary in nature, this booklet presents complete assurance of crucial fabrics in relation to the purposes of ANNs within the melanoma box. With contributions from favourite study facilities around the globe, it serves as an advent to how neural networks can be utilized for exact prediction or prognosis and indicates why neural networks are extra exact. synthetic Neural Networks in melanoma prognosis, diagnosis, and sufferer administration can provide an knowing of this new instrument, its functions, and while it's going to be used.

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Extra resources for Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management (Biomedical Engineering)

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A statistical analysis of DNA ploidy distribution by either criterion did not relate to nodal spread. This contrasts with the ANN analysis, which indicates a positive effect on neural-based prediction. The ratio G0G1/G2M indeed reflects a facet of DNA aneuploidy. The presence of >10% of cells in the G2M peak indicates a hyperdiploid state of a DNA Ploidy and Cell Cycle Distribution of Breast Cancer Aspirate Cells 25 cell population [18,19]. This possibly provides a more reliable and stringent technique for the determination of aneuploidy than by using the criterion of DNA index.

INTRODUCTION The evolution of cellular diversity, which is an inherent property of neoplastic progression, has been attributed to genetic instability. Genetic instability often manifests itself in neoplasms in the form of chromosomal and DNA aneuploidy, altered DNA repair properties, gene amplification, and deletion and point mutations. There is a general recognition that genetic instability often occurs in parallel with the generation and evolution of variant cell clones with enhanced invasive and metastatic properties.

P53 as an independent prognostic marker in lymph nodenegative breast cancer patients, J. Natl. , 85, 965-970, 1993. 42. , Prognostic significance of p53 gene alterations in node-negative breast cancer, Breast Cancer Res. , 26, 225-235, 1993. 43. , Cellular accumulation of p53 protein: an independent prognostic factor in stage II breast cancer, Eur. J. Cancer, 30A, 175-180, 1994. 20 ANNs in Cancer Diagnosis, Prognosis, and Patient Management 44. , Accumulation of p53 tumor suppressor gene protein: An independent marker of prognosis in breast cancers, J.

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Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management (Biomedical Engineering) by R. N. G. Naguib, G. V. Sherbet

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