<i>AutoPPI</i>: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction
Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protei...
Main Authors: | Gabriela Czibula, Alexandra-Ioana Albu, Maria Iuliana Bocicor, Camelia Chira |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/23/6/643 |
Similar Items
-
<i>AutoNowP</i>: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction
by: Gabriela Czibula, et al.
Published: (2021-07-01) -
Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder
by: Jun-Jie Zhu, et al.
Published: (2023-04-01) -
Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations
by: Yuan Liu, et al.
Published: (2022-08-01) -
Explore Protein Conformational Space With Variational Autoencoder
by: Hao Tian, et al.
Published: (2021-11-01) -
Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection
by: Fardina Fathmiul Alam, et al.
Published: (2020-03-01)