Abstract
Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence.
Highlights
Persistent pain is a major healthcare issue, as defined by WHO, affecting about a fifth of the European population increasing to a third in the over 70-year old (Elliott et al, 1999; Breivik et al, 2006)
The biological functions associated with the expression of these genes and their respective products were queried from the Gene Ontology knowledge base (GO; http://www.geneontology.org/; Ashburner et al, 2000)
Subsequent functional abstraction, which is a method developed to reduce a large set of GO terms to a comprehensible small subset of “headline terms” or “functional areas” that represent specific aspects of the complete polyhierarchy with maximal coverage, precision, informational value and conciseness (Ultsch and Lötsch, 2014), identified 8 GO terms qualifying as headlines to summarize the biological functions that are important addressed by the 20 genes associated with insensitivity to pain, among all human genes (Table 4)
Summary
Persistent pain is a major healthcare issue, as defined by WHO, affecting about a fifth of the European population increasing to a third in the over 70-year old (Elliott et al, 1999; Breivik et al, 2006). Computational Analysis of Pain-Relevant Genes targets from molecular research as the main line of research, scanning the existing pharmacopeia for repurposing candidates becomes increasingly successful (Ashburn and Thor, 2004) This is facilitated by developments in computational data science (President’s Information Technology Advisory Committee, 2005). An accepted source of novel options for the pharmacological treatment of (persistent) pain is the study of genes causally involved in hereditary syndromes with insensitivity to pain (Goldberg et al, 2012; Table 1). This set of genes has provided the targets of novel analgesics (Table 2). The present analysis made extensive use of computational biology, knowledge discovery methods, publicly available databases and data mining tools (Table 3) to merge results from pain, genetics and pharmacological research
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