Unexplored therapeutic opportunities in the human genome

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development. © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved

Erratum: Unexplored therapeutic opportunities in the human genome (Nature reviews. Drug discovery (2018) 17 5 (317-332))

This corrects the article DOI: 10.1038/nrd.2018.14

Completing SBGN-AF Networks by Logic-Based Hypothesis Finding

International audienceThis study considers formal methods for finding unknown interactions of incomplete molecular networks using microarray profiles. In systems biology, a challenging problem lies in the growing scale and complexity of molecular networks. Along with high-throughput experimental tools, it is not straightforward to reconstruct huge and complicated networks using observed data by hand. Thus, we address the completion problem of our target networks represented by a standard markup language, called SBGN (in particular, Activity Flow). Our proposed method is based on logic-based hypothesis finding techniques; given an input SBGN network and its profile data, missing interactions can be logically generated as hypotheses by the proposed method. In this paper, we also show empirical results that demonstrate how the proposed method works with a real network involved in the glucose repression of S. cerevisiae