Residue-Residue Interaction Prediction via Stacked Meta-Learning
| Autor: | Kuan Hsi Chen, Yuh Jyh Hu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Models Molecular Meta learning (computer science) Computer science QH301-705.5 residue–residue interaction Article Catalysis Inorganic Chemistry 03 medical and health sciences Residue (chemistry) stacked meta-learning Feature (machine learning) Physical and Theoretical Chemistry Biology (General) Amino Acids Molecular Biology QD1-999 Spectroscopy Sequence 030102 biochemistry & molecular biology Basis (linear algebra) protein complex Organic Chemistry Computational Biology General Medicine Computer Science Applications Chemistry 030104 developmental biology ROC Curve Area Under Curve Biological system human activities Algorithms |
| Zdroj: | International Journal of Molecular Sciences, Vol 22, Iss 6393, p 6393 (2021) International Journal of Molecular Sciences Volume 22 Issue 12 |
| ISSN: | 1422-0067 |
| Popis: | Protein–protein interactions (PPIs) are the basis of most biological functions determined by residue–residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta’s performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|