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This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm's efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm's robustness. The performance metrics derived from these tests — accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa — indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system's accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health. [ABSTRACT FROM AUTHOR]

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method's applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment. [ABSTRACT FROM AUTHOR]

Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation. A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely. A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family discrimination. Training and validation employed 70% of the dataset, while 30% was used for testing. This paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image (Bispectrum). Cascading minimized false positive and negative cases in all phases. The initial stage focused on two classes (six groups and ten groups). The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning classification. The singlestage technique had 64.2% +/− 22.8% accuracy, 63.3% +/− 17.1% precision, and a 63.2% +/19.4% F1-score. The two-stage technique yielded 92.2% +/− 4.9% accuracy, 92.7% +/− 7.0% precision, and a 92.3% +/− 5.0% F1-score. This work provides balanced, reliable, and precise forecasts for all families in all measures. It ensured that the new model was resilient to family variances and provided high-scoring results. [ABSTRACT FROM AUTHOR]

Proteins are essential for many biological functions. For example, folding amino acid chains reveals their functionalities by maintaining tissue structure, physiology, and homeostasis. Note that quantifiable protein characteristics are vital for improving therapies and precision medicine. The automatic inference of a protein’s properties from its amino acid sequence is called “basic structure”. Nevertheless, it remains a critical unsolved challenge in bioinformatics, although with recent technological advances and the investigation of protein sequence data. Inferring protein function from amino acid sequences is crucial in biology. This study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector representation. The power of two representations was used to identify each amino acid, and a coding technique was established for each sequence family. Subsequently, the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families. A deep Convolutional Neural Network (CNN) classifies the resulting images and developed non-normalized and normalized encoding techniques. Initially, the dataset was split 70/30 for training and testing. Correspondingly, the dataset was utilized for 70% training, 15% validation, and 15% testing. The suggested methods are evaluated using accuracy, precision, and recall. The non-normalized method had 70% accuracy, 72% precision, and 71% recall. 68% accuracy, 67% precision, and 67% recall after validation. Meanwhile, the normalized approach without validation had 92.4% accuracy, 94.3% precision, and 91.1% recall. Validation showed 90% accuracy, 91.2% precision, and 89.7% recall. Note that both algorithms outperform the rest. The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture. They offered the best inference performance as the proposed approach improves categorization and prediction. Several instances show successful multi-class prediction in molecular biology’s massive data. [ABSTRACT FROM AUTHOR]