التفاصيل البيبلوغرافية
| العنوان: |
Test results. |
| المؤلفون: |
Hiam Alquran, Amjed Al Fahoum, Ala’a Zyout, Isam Abu Qasmieh |
| سنة النشر: |
2023 |
| مصطلحات موضوعية: |
Biochemistry, Molecular Biology, Infectious Diseases, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, uses different topologies, numerous scientific disciplines, integrating bispectral analysis, frequently ineffective techniques, facilitate pharmaceutical innovation, diverse cellular systems, convolutional neural networks, deep learning techniques, deep learning strategies, utilizes bispectrum analysis, represent protein sequences, protein family identification, numerous protein datasets, +proteins%22">xlink "> proteins, outperform existing methods, integrates bispectrum characteristics, employs bispectrum characteristics, method &# 8217, machine learning algorithms, deep learning approaches, identifying protein families, classify protein families, play crucial roles, chooses robust features, advanced protein classification |
| الوصف: |
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 ... |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/dataset/Test_results_/24811175 |
| DOI: |
10.1371/journal.pone.0295805.t003 |
| الاتاحة: |
https://doi.org/10.1371/journal.pone.0295805.t003
https://figshare.com/articles/dataset/Test_results_/24811175 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.2BEC4133 |
| قاعدة البيانات: |
BASE |