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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omics data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity, and the "grouping effect" in solution to select the discriminate features with prognostic value. An iterative gradient descent algorithm is also developed to solve the model with regularized optimization. GIREN was applied to human ovarian cancer and breast cancer datasets obtained from The Cancer Genome Atlas, respectively. Result shows that, the proposed GIREN algorithm obtained more accurate and robust performance over competing algorithms (LASSO, Elastic Net, and Semi-supervised PCA, with or without average pathway expression features) in predicting cancer progression on both two datasets in terms of median area under curve (AUC) and interquartile range (IQR), suggesting a promising direction for more effective integration of gene measurement and gene interaction information.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net. 2805 97

In this work, arrhythmia appearing with the presence of abnormal heart electrical activity is efficiently recognized and classified. A novel method is proposed for accurate recognition and classification of cardiac arrhythmias. Firstly, P-QRS-T waves is segmented from ECG waveform; secondly, morphological features are extracted from P-QRS-T waves, and ECG segment features are extracted from the selected ECG segment by using PCA and dynamic time warping(DTW); finally, SVM is applied to the features and automatic diagnosis results is presented. ECG data set used is derived from the MIT-BIH in which ECG signals are divided into the four classes: normal beats(N), supraventricular ectopic beats (SVEBs), ventricular ectopic beats (VEBs) and fusion of ventricular and normal (F). Our proposed method can distinguish N, SVEBs, VEBs and F with an accuracy of 97.80 percent. The sensitivities for the classes N, SVEBs, VEBs and F are 99.27, 87.47, 94.71, and 73.88 percent and the positive predictivities are 98.48, 95.25, 95.22 and 86.09 percent respectively. The detection sensitivity of SVEBs and VEBs has a better performance by combining proposed features than by using the ECG morphology or ECG segment features separately. The proposed method is compared with four selected peer algorithms and delivers solid results.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis. 2999 63