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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Ranking support vector machine for multiple kernels output combination in protein-protein interaction extraction from
Zhihao Yang1, Yuan Lin, Jiajin Wu
1College of Computer Science and Technology, Dalian University of Technology, Dalian, P. R. China. yangzh@dlut.edu.cn
Proteomics
|August 12, 2011
Summary
This study introduces a novel multiple kernel learning approach for automatically extracting protein-protein interactions (PPIs) from biomedical literature. The method significantly improves the accuracy of identifying these crucial biological interactions.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Bioinformatics
Background:
- Understanding protein-protein interactions (PPIs) is vital for elucidating biological mechanisms.
- The rapid growth of biomedical literature poses challenges for manual curation of PPI databases.
- Automated methods are needed to efficiently extract PPI information.
Purpose of the Study:
- To develop and evaluate an automated approach for extracting protein-protein interactions from biomedical texts.
- To improve the efficiency and accuracy of PPI database curation.
Main Methods:
- A multiple kernel learning framework was employed for PPI extraction.
- The approach integrated feature-based, tree, and graph kernels.
- A Ranking Support Vector Machine (SVM) was used to combine kernel outputs.
Main Results:
- Individual kernels demonstrated complementary features for PPI extraction.
- The combined kernel approach with Ranking SVM outperformed individual kernels and simple weight combinations.
- The method achieved state-of-the-art performance with a 64.88% F-score and 88.02% AUC on the AImed corpus.
Conclusions:
- The proposed multiple kernel learning approach effectively automates protein-protein interaction extraction from literature.
- This method offers a significant advancement for curating large-scale PPI databases.
- The findings highlight the utility of integrating diverse features for enhanced biological information extraction.
Related Concept Videos
02:04Protein-protein Interfaces
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
02:04Protein-Protein Interfaces
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:26Protein Networks
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These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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