Sumayia Al-Anazi; Isra Al-Turaiki; Najwa Altwaijry
Abstract
Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ...
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Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ensemble methods. A review and classification of current ensemble motif discovery tools is presented. We then propose our Cluster-based Ensemble Motif Discovery Tool (CEMD) which is based on k-medoids clustering of state-of-art stand-alone motif finding tools. We evaluate the performance of CEMD on benchmark datasets and compare the results to both stand-alone and similar ensemble tools. Experimental results indicate that CEMD has better sensitivity than state-of-art stand-alone tools when dealing with human datasets. CEMD also obtains better values of sensitivity when motifs are implanted in real promoter sequences. As for the comparison of CEMD with ensemble motif discovery tools, results indicate that CEMD achieves better results than MEME-ChIP on all evaluation measures. CEMD shows comparable performance to RSAT peak-motifs and MODSIDE.
Isra Al-Turaiki; Najwa Altwaijry; Abeer Agil; Haya Aljodhi; sara Alharbi; Lina Alqassem
Abstract
With present-day technological advancements, the number of devices connected to the Internet has increased dramatically. Cybersecurity attacks are increasingly becoming a threat to individuals and organizations. Contemporary security frameworks incorporate Network Intrusion Detection Systems (NIDS). ...
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With present-day technological advancements, the number of devices connected to the Internet has increased dramatically. Cybersecurity attacks are increasingly becoming a threat to individuals and organizations. Contemporary security frameworks incorporate Network Intrusion Detection Systems (NIDS). These systems are an essential component for ensuring the security of computer networks against attacks. In this paper, two deep learning architectures are proposed for both binary and multi-class classification of network attacks. The models, CNN-IDS and LSTM-IDS, are based on Convolutional Neural Network and Long Short Term Memory architectures, respectively. The models are evaluated using the well-known NSL-KDD dataset. The performance is measured in terms of accuracy, precision, recall, and F-measure. Experimental results show that the models achieve good performance in terms of accuracy and recall. Network intrusion detection systems are an integral part of contemporary networks. They provide administrators with an early warning for known and unknown attacks. In this paper, two deep learning architectures to aid administrators in detecting network attacks are outlined