Hanaa Torkey

Post-Translational Modifications (PTMs) are covalent chemical alterations that occur after protein synthesis, critically regulating protein function, localization, and interactions. β-hydroxybutyrylation (Kbhb), a metabolically derived histone modification discovered in 2016, influences gene activation and cellular metabolism. While accurate PTM site identification is essential for understanding protein regulation and disease mechanisms, experimental approaches face significant limitations, including low modification abundance, high cost, and limited proteome coverage. Kbhb remains computationally underexplored, with only three existing prediction tools exhibiting modest accuracy and limited cross-species applicability. To address this gap, we developed BiGKbhb, a deep learning framework that depends on Bidirectional Gated Recurrent Units (BiGRU). With BiGKbhb, we systematically evaluate seven protein sequence encoding strategies, and compare six deep learning architectures using datasets from human, mouse, and fungal species. Results demonstrated that BLOSUM62 evolutionary encoding combined with BiGRU architecture achieves optimal performance, with BiGKbhb consistently achieving higher accuracy than those of existing methods with test set accuracies of 0.824, 0.832, and 0.871 for human, mouse, and fungal balanced datasets, respectively, with corresponding Area Under Curve (AUC) values of 0.920, 0.902, and 0.945, while additional evaluation on imbalanced datasets confirmed model robustness under realistic conditions. Cross-species analysis revealed enhanced transferability of the general multi-species model, and statistical validation confirmed significant improvements over existing predictors ( p < 0.05). These findings contribute a robust computational tool for Kbhb prediction and provide insights into sequence determinants of this important modification across evolutionarily diverse species.

Lysine 2-hydroxyisobutyrylation (Khib) has emerged as a crucial Post-Translational Modification (PTM) with significant roles in diverse biological processes ranging from gene expression to metabolic regulation. Despite its importance, computational approaches for accurately predicting Khib sites remain limited. This study introduces BLOS-Khib, a deep-learning framework that utilizes evolutionary information encoded in the BLOSUM62 matrix within a Convolutional Neural Network (CNN) architecture for cross-species Khib site prediction. Through systematic optimization, we found that a 43-amino acid peptide length captures the optimal sequence context for prediction across six taxonomically diverse organisms. Comprehensive comparative analyses demonstrated BLOS-Khib competitive performance compared to existing methods, achieving notable Area Under the ROC Curve (AUC) values on independent test sets: human (0.913), wheat (0.892), T. gondii (0.893), rice (0.887), Candida albicans (0.885), and Botrytis cinerea (0.903). Our framework showed improved performance compared to state-of-the-art approaches, including traditional machine learning classifiers and alternative deep learning architectures. Sequence signature analysis revealed both conserved lysine-rich regions preceding modification sites and species-specific amino acid preferences at positions immediately flanking the target residue. Notably, our cross-species applicability experiments identified high transferability between evolutionarily distant organisms, ensuring the potential convergent evolution of Khib determinants. BLOS-Khib demonstrates competitive performance for PTM prediction, while providing evolutionary insights into the sequence determinants governing this emerging regulatory mechanism across diverse species.

Breast cancer is the second most prevalent cancer globally and remains one of the leading causes of cancer-related mortality. Over the past few decades, the incidence of breast cancer has increased significantly, highlighting the critical need for early detection to improve survival rates. In response, researchers have been actively developing computer-aided diagnostic systems to assist in rapid and accurate diagnosis. Various datasets have been utilized in these efforts, leveraging the power of Artificial Intelligence (AI) to support radiologists in medical image analysis, ultimately enhancing patient diagnosis and treatment. Among the available diagnostic techniques, histopathology imaging remains the gold standard for detecting breast cancer with high accuracy. In this study, we employed ResNet- based architectures to implement a Mask Region-based Convolutional Neural Network (Mask R-CNN) for the automated detection of nuclei in histopathological breast cancer images. Following detection, the system classifies the cancer type, extracting multi-scale features using a combination of Feature Pyramid Networks (FPN) modules. To further enhance recognition accuracy, we utilized Region of Interest Align(RoIAlign), ensuring precise feature extraction. Experimental results demonstrate that our proposed approach not only delivers superior visual interpretability but also outperforms existing models in key performance metrics, achieving 97.7% accuracy, 97% recall, and a 96.7% F1 score.

The exponential growth of interconnected devices in the Internet of Things (IoT) has raised significant concerns about data security, especially when transmitting sensitive information over wireless channels. Traditional encryption techniques often fail to meet the energy and processing constraints of resource-limited IoT devices. This paper proposes a novel hybrid security framework that integrates chaotic encryption and steganography to enhance confidentiality, integrity, and resilience in audio communication. Chaotic systems generate unpredictable keys for strong encryption, while steganography conceals the existence of sensitive data within audio signals, adding a covert layer of protection. The proposed approach is evaluated within an Orthogonal Frequency Division Multiplexing (OFDM)-based wireless communication system, widely recognized for its robustness against interference and channel impairments. By combining secure encryption with a practical transmission scheme, this work demonstrates the effectiveness of the proposed hybrid method in realistic IoT environments, achieving high performance in terms of signal integrity, security, and resistance to noise. Simulation results indicate that the OFDM system incorporating chaotic algorithm modes alongside steganography outperforms the chaotic algorithm alone, particularly at higher Eb/No values. Notably, with DCT-OFDM, the chaotic-CFB based on steganography algorithm achieves a performance gain of approximately 30 dB compared to FFT-OFDM and DWT-based systems at Eb/No = 8 dB. These findings suggest that steganography plays a crucial role in enhancing secure transmission, offering greater signal deviation, reduced correlation, a more uniform histogram, and increased resistance to noise, especially in high BER scenarios. This highlights the potential of hybrid cryptographic-steganographic methods in safeguarding sensitive audio information within IoT networks and provides a foundation for future advancements in secure IoT communication systems.

The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer’s Disease, Brain Cancer, Stroke, and Epilepsy. Epilepsy, a neurological disorder marked by recurrent seizures, results from irregular electrical activity in the brain. These seizures, which can strain both patients and neurologists, are characterized by symptoms like the loss of awareness, unusual behavior, and confusion. This study presents an efficient EEG-based epileptic seizure detection framework utilizing a hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models approach to support automated and accurate diagnosis. Handling imbalanced EEG data, which can otherwise bias model outcomes and reduce predictive accuracy, is a key focus. Experimental results indicate that the proposed framework generally outperforms other Deep Learning and Machine Learning techniques with the highest accuracy at 99.13%. Likewise, an Explainable Artificial Intelligence (XAI) called SHAP (SHapley Additive exPlanations) is utilized to analyze the results and to improve the interpretability of the models from medical decision-making. This framework aligns with the objectives of the Medical Internet of Things (MIoT), advancing smart medical applications and services for effective epileptic seizure detection.

Background: Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately.Methods: This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein’s characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions.Results: Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.

Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas’s most recent statistics, there are more than 2.8 million people worldwide diagnosed with MS. Recently, studies started to explore machine learning techniques to predict MS using various data. The objective of this paper is to develop an ensemble approach for diagnosis of MS using gene expression profiles, while handling the class imbalance problem associated with the data. A hierarchical ensemble approach employing voting and boosting techniques is proposed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81% and 93.5% with BoostFS and DEGs for feature selection, respectively. Conclusively, the proposed approach is able to efficiently diagnose MS using the gene expression profiles that are more relevant to the disease. The approach is not merely an ensemble classifier outperforming previous work; it also identifies differentially expressed genes between normal samples and patients with multiple sclerosis using a genome-wide expression microarray. The results obtained show that the proposed approach is an efficient diagnostic tool for MS.