Literature DB >> 28131723

Protein biomarker druggability profiling.

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 Â
© 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Druggability profiling; Machine learning; Mechanistic annotation; Neonatal sepsis; Pathway analysis; Protein biomarkers

Mesh:

Substances:

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


  38 in total

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