Name
Title | ||
|---|---|---|
Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques. |
Abstract | ||
|---|---|---|
In this study, we proposed a machine learning techniques that uses low-dimensional sparse modeling to predict the PPB value of cyclic peptides computationally. The low-dimensional sparse model not only exhibits excellent generalization performance but also improves interpretation of the prediction model. This can provide common an noteworthy knowledge for future cyclic peptide drug discovery studies. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2529-z | BMC Bioinformatics |
Keywords | Field | DocType |
Biostability,Cyclic peptide,Feature selection,Plasma protein binding (PPB),Sparse modeling | Interpretability,Drug discovery,Biology,Biological system,Feature selection,Lasso (statistics),Beam search,Small molecule,Cyclic peptide,Overfitting,Genetics | Journal |
Volume | Issue | ISSN |
19 | Suppl 19 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Tajimi | 1 | 0 | 0.34 |
| Naoki Wakui | 2 | 0 | 0.34 |
| Keisuke Yanagisawa | 3 | 0 | 0.34 |
| Yasushi Yoshikawa | 4 | 0 | 0.34 |
| Masahito Ohue | 5 | 28 | 8.17 |
| Yutaka Akiyama | 6 | 172 | 37.62 |