Sharif Digital Repository

Patterns on proteins and genomic sequences are vastly analyzed, extracted and collected in databases. Although protein patterns originate from genomic coding regions, very few works have directly or indirectly dealt with coding region patterns induced from protein patterns. Results: In this paper, we have defined a new genomic pattern structure suitable for representing induced patterns from proteins. The provided pattern structure, which is called "Consecutive Positions Scoring Matrix (CPSSM)", is a replacement for protein patterns and profiles in the genomic context. CPSSMs can be identified, discovered, and searched in genomes. Then, we have presented a novel pattern matching algorithm... 

In this paper, an efficient approach for ultrasonic object segmentation with special application for left ventricle segmentation in echocardiography images is proposed. At first, an efficient hybrid trend for ultrasonic image edge detection is suggested. Then, a modified level set approach is introduced based on the extracted edges and the computed probabilistic map as the stopping criteria for the contour evolution. Both synthetic and clinical images are utilized as validation measures with respect to the prior techniques which indicate outperform results quantitatively and qualitatively. Left ventricle segmentation using proposed method illustrates expert-approved performance, providing a... 

Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients’ features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis...