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Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families.

Zi-Mei Zhang¹, Zheng-Xing Guan¹, Fang Wang¹

¹Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Medicinal Chemistry (Shariqah (United Arab Emirates))

|October 5, 2019

Summary

Machine learning effectively predicts nuclear receptors (NRs) and their subfamilies, crucial for understanding cellular functions. This bioinformatics approach accelerates NR classification, overcoming limitations of traditional experimental methods.

Keywords:

NRs families Nuclear receptors (NRs)classification feature selection machine learning methods prediction

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Area of Science:

Genomics and Bioinformatics
Molecular Biology
Computational Biology

Background:

Nuclear receptors (NRs) are critical transcription factors regulating diverse cellular processes.
NRs are classified into distinct subfamilies based on conserved domains, each with unique functions.
Accurate NR identification is vital for understanding normal and pathological cellular mechanisms.

Purpose of the Study:

To review the application of machine learning (ML) methods for predicting NRs and their subfamilies.
To highlight the need for efficient bioinformatics tools in NR classification.
To provide a reference for future research in NR identification and family classification.

Main Methods:

Review of existing literature on machine learning applications in NR prediction.
Analysis of different ML approaches for NR and subfamily identification.
Discussion of the advantages of computational methods over experimental techniques.

Main Results:

Machine learning methods offer a powerful and efficient approach for NR prediction.
Bioinformatics tools can rapidly identify NRs and their subfamilies from large sequence datasets.
ML-based prediction aids in understanding NR functions and biological roles.

Conclusions:

Machine learning significantly enhances the speed and accuracy of NR classification.
Computational approaches are essential for navigating the data deluge in the post-genomics era.
This review underscores the utility of ML in advancing NR research and functional genomics.