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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction |
Identification Number: |
doi:10.21979/N9/MEDJN1 |
Distributor: |
DR-NTU (Data) |
Date of Distribution: |
2023-06-23 |
Version: |
1 |
Bibliographic Citation: |
Wee, JunJie; Xia, Kelin, 2023, "Replication Data for: Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction", https://doi.org/10.21979/N9/MEDJN1, DR-NTU (Data), V1 |
Citation |
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Title: |
Replication Data for: Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction |
Identification Number: |
doi:10.21979/N9/MEDJN1 |
Authoring Entity: |
Wee, JunJie (Nanyang Technological University) |
Xia, Kelin (Nanyang Technological University) |
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Software used in Production: |
Python |
Software used in Production: |
MATLAB |
Software used in Production: |
numpy |
Software used in Production: |
scipy |
Software used in Production: |
scikit-learn |
Software used in Production: |
GUDHI |
Grant Number: |
M4081842.110 |
Grant Number: |
RG109/19 |
Grant Number: |
MOE-T2EP20120-0013 |
Grant Number: |
MOE-T2EP20220-0010 |
Distributor: |
DR-NTU (Data) |
Access Authority: |
Xia, Kelin |
Depositor: |
Wee, JunJie |
Date of Deposit: |
2023-06-12 |
Holdings Information: |
https://doi.org/10.21979/N9/MEDJN1 |
Study Scope |
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Keywords: |
Computer and Information Science, Mathematical Sciences, Medicine, Health and Life Sciences, Computer and Information Science, Mathematical Sciences, Medicine, Health and Life Sciences, Protein-protein interaction, Hodge Laplacian, Persistent spectral, Molecular featurization, Ensemble learning |
Abstract: |
Protein–protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a great challenge for all learning models for PPIs. Here, we propose persistent spectral (PerSpect) based PPI representation and featurization, and PerSpect-based ensemble learning (PerSpect-EL) models for PPI binding affinity prediction, for the first time. In our model, a sequence of Hodge (or combinatorial) Laplacian (HL) matrices at various different scales are generated from a specially designed filtration process. PerSpect attributes, which are statistical and combinatorial properties of spectrum information from these HL matrices, are used as features for PPI characterization. Each PerSpect attribute is input into a 1D convolutional neural network (CNN), and these CNN networks are stacked together in our PerSpect-based ensemble learning models. 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 can achieve state-of-the-art results and outperform all existing models to the best of our knowledge. |
Kind of Data: |
Calculated data and codes |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1093/bib/bbac024 |
Bibliographic Citation: |
Wee, J., & Xia, K. (2022). Persistent spectral based ensemble learning (PerSpect-EL) for protein–protein binding affinity prediction. Briefings in Bioinformatics, 23(2). |
Citation |
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Identification Number: |
10356/162232 |
Bibliographic Citation: |
Wee, J. & Xia, K. (2022). Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction. Briefings in Bioinformatics, 23(2). |
Label: |
PerSpect-Ensemble-Learning.rar |
Notes: |
application/x-rar-compressed |