Highlights: Linear hybridization of statistical learning and machine learning methods can increase prediction accuracy. LRM can evaluate the contribution of genomic aberrations detected by CGH.
Book Title: Prediction of Metastasis in Advanced Colorectal Carcinomas Using CGH Data
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Published on 2017 by
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Book is About: Carcinoma
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Highlights: Linear hybridization of statistical learning and machine learning methods can increase prediction accuracy. LRM can evaluate the contribution of genomic aberrations detected by CGH. Hybrid modeling can improve prediction of metastasis in colorectal carcinomas. Abstract: Logistic Regression Model (LRM) and artificial neural networks (ANNs) as two nonlinear models have been used to establish a novel two-stage hybrid modeling procedure for prediction of metastasis in advanced colorectal carcinomas. Two different datasets were used in training and testing procedures. For the first stage of hybrid modeling procedure, LRM was used to evaluate the contribution of DNA sequence copy number aberrations detected by Comparative Genomic Hybridization in advanced colorectal carcinoma and its metastasis. Then, the most effective parameters were selected by the LRM. Selected effective parameters among 565 detected chromosomal gains and losses were as follows: gain of 20q11.2, loss of 1q42, loss of 13q34, gain of 5q12, gain of 17p13, loss of 2q22, loss of 11q24 and gain of 2p11.2. Consequently, neural network models were constructed and fed by the parameters selected by LRM to build hybrid predictors on the two databases during self-consistency and jackknife tests, and performance of the hybrid model was verified. The results showed that our two-stage hybrid model approach is very promising for prediction of metastasis in advanced colorectal carcinomas.
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