Protein Biomarker Druggability Profiling
| Autor: | Oleg Ursu, Subramani Mani, Stephen L. Mathias, Daniel C. Cannon, Cristian Bologa, Karri Ballard, Tudor I. Oprea, Robin K. Ohls |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Proteomics Protein biomarkers Druggability Health Informatics Bioinformatics Article 03 medical and health sciences 0302 clinical medicine medicine Profiling (information science) Humans Proteomic Profile Neonatal sepsis business.industry Infant Newborn medicine.disease Kegg pathway Computer Science Applications 030104 developmental biology Biomarker (medicine) Neonatal Sepsis business 030217 neurology & neurosurgery Algorithms Biomarkers |
| Popis: | Display Omitted An interactive druggability profiling algorithm for protein biomarkers is proposed.Top ranked biomarkers are identified using computational machine learning methods.Mechanistic annotation derived from KEGG pathway and DrugCentral databases.A visual representation for proposing new drug targets and drug-target evaluation.Tissue specificity of the biomarkers determined by target tissue localization. Developing automated and interactive methods for building a model by incorporating mechanistic and potentially causal annotations of ranked biomarkers of a disease or clinical condition followed by a mapping into a contextual framework in disease-linked biochemical pathways can be used for potential drug-target evaluation and for proposing new drug targets. We demonstrate the potential of this approach using ranked protein biomarkers obtained in neonatal sepsis by enrolling 127 infants (39 infants with late onset neonatal sepsis and 88 control infants) and by performing a focused proteomic profile of the sera and by applying the interactive druggability profiling algorithm (DPA) developed by us. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|