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Ion channels are transmembrane proteins that selectively allow ions to flow across the plasma membrane and play key roles in diverse biological processes. A multitude of diseases, called channelopathies, such as epilepsies, muscle paralysis, pain syndromes, cardiac arrhythmias or hypoglycemia are due to ion channel mutations. A wide corpus of literature is available on ion channels, covering both their functions and their roles in disease. The research community needs to access this data in a user-friendly, yet systematic manner. However, extraction and integration of this increasing amount of data have been proven to be difficult because of the lack of a standardized vocabulary that describes the properties of ion channels at the molecular level. To address this, we have developed Ion Channel ElectroPhysiology Ontology (ICEPO), an ontology that allows one to annotate the electrophysiological parameters of the voltage-gated class of ion channels. This ontology is based on a three-state model of ion channel gating describing the three conformations/states that an ion channel can adopt: closed, open and inactivated. This ontology supports the capture of voltage-gated ion channel electrophysiological data from the literature in a structured manner and thus enables other applications such as querying and reasoning tools. Here, we present ICEPO (ICEPO ftp site:ftp://ftp.nextprot.org/pub/current_release/controlled_vocabularies/), as well as examples of its use. © The Author(s) 2016. Published by Oxford University Press.

Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological framework. The ICEPO ontology is available for download at http://openbionlp.org/mutd/supplementarydata/ICEPO/ICEPO.owl.