Name
Abstract | ||
|---|---|---|
The study of protein dynamics through analysis of conformational transitions represents a significant stage in understanding protein function. Using molecular simulations, large samples of protein transitions can be recorded. However, extracting functional motions from these samples is still not automated and extremely time-consuming. In this paper we investigate the usefulness of unsupervised machine learning methods for uncovering relevant information about protein functional dynamics. Autoencoders are being explored in order to highlight their ability to learn relevant biological patterns, such as structural characteristics. This study is aimed to provide a better comprehension of how protein conformational transitions are evolving in time, within the larger framework of automatically detecting functional motions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01421-6_8 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II |
Keywords | Field | DocType |
Protein molecular dynamics, Autoencoders, Unsupervised learning | Pattern recognition,Computer science,Protein dynamics,Unsupervised learning,Protein function,Artificial intelligence,Comprehension,Machine learning | Conference |
Volume | ISSN | Citations |
11140 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mihai Teletin | 1 | 4 | 2.53 |
| Gabriela Czibula | 2 | 80 | 19.53 |
| Maria-Iuliana Bocicor | 3 | 6 | 1.52 |
| Silvana Albert | 4 | 3 | 1.47 |
| Alessandro Pandini | 5 | 49 | 4.42 |