Mohammad Nassef

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Genome resequencing produces enormous amount of data daily. Biologists need to frequently mine this data with the provided processing and storage resources. Therefore, it becomes very critical to professionally store this data in order to efficiently browse it in a frequent manner. Reference-based Compression algorithms (RbCs) showed significant genome(More)
Genome banks contain precious biological information that is mostly not discovered yet. Biologists in turn are keen to precisely explore these banks in order to discover effective patterns (such as motifs and retro-transposons) that have a real impact on the function and evolution of living creatures. Because the modern genome sequencing technologies(More)
Matrix factorization is one of the best approaches for collaborative filtering, because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and being very hard to be parallelized, specially with very large matrices. In this paper, we introduce a new method for collaborative(More)
The paper presents approaches for nodule detection and extraction in axial lung computed tomography. The goal is to detect correctly pulmonary nodule to recognize and screen lung cancer patients. The pulmonary nodule detection is very challenging problem. The proposed model developed a hybrid efficient model based on affine-invariant representation and(More)
Feature selection methods for cancer classification are aimed to overcome the high dimensionality of the biomedical data which is a challenging task. Most of the feature selection methods based on DNA methylation are time consuming during testing phase to identify the best pertinent features subset that are relevant to accurate prediction. However, the(More)
BACKGROUND Chronic diseases are becoming more serious and widely spreading and this carries a heavy burden on doctors to deal with such patients. Although many of these diseases can be treated by bacteriophages, the situation is significantly dangerous in patients having concomitant more than one chronic disease, where conflicts between phages used in(More)
This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The(More)