| Literature DB >> 28131723 |
Subramani Mani1, Daniel Cannon2, Robin Ohls3, Tudor Oprea4, Stephen Mathias5, Karri Ballard6, Oleg Ursu7, Cristian Bologa8.
Abstract
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. Copyright ÂEntities:
Keywords: Druggability profiling; Machine learning; Mechanistic annotation; Neonatal sepsis; Pathway analysis; Protein biomarkers
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Year: 2017 PMID: 28131723 PMCID: PMC6286812 DOI: 10.1016/j.jbi.2017.01.014
Source DB: PubMed Journal: J Biomed Inform ISSN: 1532-0464 Impact factor: 6.317