Persistent tor-algebra based stacking ensemble learning (PTA-SEL) for protein-protein binding affinity prediction

Xiang Liu, Kelin Xia
Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, PMLR 196:237-247, 2022.

Abstract

Protein-protein interactions (PPIs) play crucial roles in almost all biological processes. Recently, data-driven machine learning models have shown great power in the analysis of PPIs. However, efficient molecular representation and featurization are still key issues that hinder the performance of learning models. Here, we propose persistent Tor-algebra (PTA), PTA-based molecular characterization and featurization, and PTA-based stacking ensemble learning (PTA-SEL) for PPI binding affinity prediction, for the first time. More specifically, the Vietoris-Rips complex is used to characterize the PPI structure and its persistent Tor-algebra is computed to form the molecular descriptors. These descriptors then are fed into our stacking model to make the prediction. We systematically test our model on the two most commonly used datasets, i.e., SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great power of our model.

Cite this Paper

BibTeX
@InProceedings{pmlr-v196-liu22a, title = {Persistent Tor-Algebra Based Stacking Ensemble Learning (PTA-SEL) for Protein-Protein Binding Affinity Prediction}, author = {Liu, Xiang and Xia, Kelin}, booktitle = {Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022}, pages = {237--247}, year = {2022}, editor = {Cloninger, Alexander and Doster, Timothy and Emerson, Tegan and Kaul, Manohar and Ktena, Ira and Kvinge, Henry and Miolane, Nina and Rieck, Bastian and Tymochko, Sarah and Wolf, Guy}, volume = {196}, series = {Proceedings of Machine Learning Research}, month = {25 Feb--22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v196/liu22a/liu22a.pdf}, url = {https://proceedings.mlr.press/v196/liu22a.html}, abstract = {Protein-protein interactions (PPIs) play crucial roles in almost all biological processes. Recently, data-driven machine learning models have shown great power in the analysis of PPIs. However, efficient molecular representation and featurization are still key issues that hinder the performance of learning models. Here, we propose persistent Tor-algebra (PTA), PTA-based molecular characterization and featurization, and PTA-based stacking ensemble learning (PTA-SEL) for PPI binding affinity prediction, for the first time. More specifically, the Vietoris-Rips complex is used to characterize the PPI structure and its persistent Tor-algebra is computed to form the molecular descriptors. These descriptors then are fed into our stacking model to make the prediction. We systematically test our model on the two most commonly used datasets, i.e., SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great power of our model.} }
Endnote
%0 Conference Paper %T Persistent tor-algebra based stacking ensemble learning (PTA-SEL) for protein-protein binding affinity prediction %A Xiang Liu %A Kelin Xia %B Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022 %C Proceedings of Machine Learning Research %D 2022 %E Alexander Cloninger %E Timothy Doster %E Tegan Emerson %E Manohar Kaul %E Ira Ktena %E Henry Kvinge %E Nina Miolane %E Bastian Rieck %E Sarah Tymochko %E Guy Wolf %F pmlr-v196-liu22a %I PMLR %P 237--247 %U https://proceedings.mlr.press/v196/liu22a.html %V 196 %X Protein-protein interactions (PPIs) play crucial roles in almost all biological processes. Recently, data-driven machine learning models have shown great power in the analysis of PPIs. However, efficient molecular representation and featurization are still key issues that hinder the performance of learning models. Here, we propose persistent Tor-algebra (PTA), PTA-based molecular characterization and featurization, and PTA-based stacking ensemble learning (PTA-SEL) for PPI binding affinity prediction, for the first time. More specifically, the Vietoris-Rips complex is used to characterize the PPI structure and its persistent Tor-algebra is computed to form the molecular descriptors. These descriptors then are fed into our stacking model to make the prediction. We systematically test our model on the two most commonly used datasets, i.e., SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great power of our model.
APA
Liu, X. & Xia, K.. (2022). Persistent tor-algebra based stacking ensemble learning (PTA-SEL) for protein-protein binding affinity prediction. Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, in Proceedings of Machine Learning Research 196:237-247 Available from https://proceedings.mlr.press/v196/liu22a.html.

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