Search

Your search keyword '"Naseer Sheraz"' showing total 29 results
Start Over Author "Naseer Sheraz"
29 results on '"Naseer Sheraz"'

4. NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule

Author

Daanial Khan Yaser, Naseer Sheraz, Hussain Waqar, and Rasool Nouman

Subjects
Computational Mathematics, Palmitoylation, Chemistry, Genetics, Computational biology, Molecular Biology, Biochemistry
Abstract

Background: Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, its identification is very important. Objective: The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identify these sites, in an easy manner. Herein, we propose a novel prediction model for the identification of N-Palmitoylation sites in proteins. Method: The proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing a prediction model to perform classification. Results: Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed the highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors. Conclusion: The proposed GRU based RNN model can help to identify N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

Published
2021

5. Comprehensive Analysis of Key Proteins Involved in Radioresistance of Prostate Cancer by Integrating Protein-protein Interaction Networks

Author

Li Dujian, Németh Balázs, Gyöngyi Zoltán, Karádi Kázmér, Wang Xiaoxia, Memarpour Mahtab, Rasool Nouman, Hegyi Péter, Marta Katalin, Révész Péter, Tóth Barbara, Luan Yun, Taghvamanesh Sara, Tulchinsky Mark, Slonimsky Einat, Sang-Ngoen Thanyaporn, Qian Duocheng, Varga Gábor, Shao Jia, Liu Dongsen, Naseer Sheraz, M. Czumbel László, Shahbaz Umar, Li Quan, H. Morowvat Mohammad, Liu Xiaoli, Mátrai Péter, Sui Shujian, Gerber Gábor, Li Yinuo, Sun Yongchao, Gao Qi, Zhang Shanshan, Karami Forough, Su Hao, Zhu Yansong, Xu Chaoyue, Hussain Waqar, Karami Somayeh, Sadaeng Wuttapon, Szanyi István, Rafiee Azade, Zhang Zhaohua, and Daanial Khan Yaser

Subjects
Computational Mathematics, Prostate cancer, Radioresistance, Genetics, medicine, Key (cryptography), Computational biology, Biology, medicine.disease, Molecular Biology, Biochemistry, Protein protein interaction network
Abstract

Background: Radioresistance remains a significant obstacle in the treatment of prostate cancer (PCa). The mechanisms underlying the radioresistance in PCa remained to be further investigated. Methods: GSE53902 dataset was used in this study to identify radioresistance-related mRNAs. Protein-protein interaction (PPI) network was constructed based on STRING analysis. DAVID system was used to predict the potential roles of radioresistance-related mRNAs. Results: We screened and re-annotated the GSE53902 dataset to identify radioresistance-related mRNAs. A total of 445 up-regulated and 1036 down-regulated mRNAs were identified in radioresistance PCa cells. Three key PPI networks consisting of 81 proteins were further constructed in PCa. Bioinformatics analysis revealed that these genes were involved in regulating MAP kinase activity, response to hypoxia, regulation of the apoptotic process, mitotic nuclear division, and regulation of mRNA stability. Moreover, we observed that radioresistance-related mRNAs, such as PRC1, RAD54L, PIK3R3, ASB2, FBXO32, LPAR1, RNF14, and UBA7, were dysregulated and correlated to the survival time in PCa. Conclusion: We thought this study would be useful to understand the mechanisms underlying radioresistance of PCa and identify novel prognostic markers for PCa.

Published
2021

18. iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions.

Author

Naseer, Sheraz, Hussain, Waqar, Khan, Yaser Daanial, and Rasool, Nouman

Abstract

Among all the PTMs, the protein phosphorylation is pivotal for various pathological and physiological processes. About 30 percent of eukaryotic proteins undergo the phosphorylation modification, leading to various changes in conformation, function, stability, localization, and so forth. In eukaryotic proteins, phosphorylation occurs on serine (S), Threonine (T) and Tyrosine (Y) residues. Among these all, serine phosphorylation has its own importance as it is associated with various importance biological processes, including energy metabolism, signal transduction pathways, cell cycling, and apoptosis. Thus, its identification is important, however, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel predictor for identification of Phosphoserine sites (PhosS) in proteins, by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep features. We used well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Among different DNNs, the best score is shown by Covolutional Neural Network based model which renders CNN based prediction model the best for Phosphoserine prediction. Based on these results, it is concluded that the proposed model can help to identify PhosS sites in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

27. Sequence-based Identification of Arginine Amidation Sites in Proteins Using Deep Representations of Proteins and PseAAC

Author

Naseer, Sheraz, Hussain, Waqar, Khan, Yaser D., and Rasool, Nouman

Abstract

Background: Among all the major post-translational modifications, amidation seems to be a small change, where a peptide ends with an amide group (-NH 2), not a carboxyl group (-COOH). Thus, to study their physicochemical properties, identification of the amidation mechanism is very important. However, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Objectives: Herein, we propose a novel predictor for the identification of arginine amide (R-Amide) sites in proteins, by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep features. Methods: We use well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Results: Among different DNNs, CNN showed the highest scores in terms of accuracy, and all other computed measures outperformed all the previously reported predictors. Conclusion: Based on these results, it is concluded that the proposed model can help identify arginine amidation in a very efficient and accurate manner, which can help scientists understand the mechanism of this modification in proteins.

Published
2020
Full Text
View/download PDF

28. Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks.

Author

Naseer, Sheraz and Saleem, Yasir

Subjects
INTRUSION detection systems (Computer security), ARTIFICIAL neural networks, COMPUTER network security, DATA mining, NEURAL computers
Abstract

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results