Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jbi.2017.01.014 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Druggability profiling,Machine learning,Mechanistic annotation,Neonatal sepsis,Pathway analysis,Protein biomarkers | Data mining,Druggability,Proteomics,Computer science,Profiling (computer programming),KEGG,Biomarker (medicine),Pathway analysis,Bioinformatics,Proteomic Profile,Neonatal sepsis | Journal |
Volume | Issue | ISSN |
66 | C | 1532-0464 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S Mani | 1 | 391 | 30.95 |
| Daniel C. Cannon | 2 | 10 | 1.89 |
| Robin Ohls | 3 | 0 | 0.34 |
| Tudor I Oprea | 4 | 359 | 46.89 |
| Stephen L. Mathias | 5 | 36 | 4.80 |
| Karri Ballard | 6 | 0 | 0.34 |
| Oleg Ursu | 7 | 7 | 2.88 |
| Cristian Bologa | 8 | 29 | 3.54 |