Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.
An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2010
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| _version_ | 1826277964687343616 |
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| author | Dobchev, D Mäger, I Tulp, I Karelson, G Tamm, T Tämm, K Jänes, J Langel, U Karelson, M |
| author_facet | Dobchev, D Mäger, I Tulp, I Karelson, G Tamm, T Tämm, K Jänes, J Langel, U Karelson, M |
| author_sort | Dobchev, D |
| collection | OXFORD |
| description | An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features. |
| first_indexed | 2024-03-06T23:36:50Z |
| format | Journal article |
| id | oxford-uuid:6df42371-4810-4e44-b4af-54f1a35e75b9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:36:50Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:6df42371-4810-4e44-b4af-54f1a35e75b92022-03-26T19:21:08ZPrediction of Cell-Penetrating Peptides Using Artificial Neural Networks.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6df42371-4810-4e44-b4af-54f1a35e75b9EnglishSymplectic Elements at Oxford2010Dobchev, DMäger, ITulp, IKarelson, GTamm, TTämm, KJänes, JLangel, UKarelson, MAn investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features. |
| spellingShingle | Dobchev, D Mäger, I Tulp, I Karelson, G Tamm, T Tämm, K Jänes, J Langel, U Karelson, M Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. |
| title | Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. |
| title_full | Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. |
| title_fullStr | Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. |
| title_full_unstemmed | Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. |
| title_short | Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. |
| title_sort | prediction of cell penetrating peptides using artificial neural networks |
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