title: Grouping chemicals for health risk assessment: A text mining-based case study of polychlorinated biphenyls (PCBs).
abstract: As many chemicals act as carcinogens, chemical health risk assessment is critically important. A notoriously time consuming process, risk assessment could be greatly supported by classifying chemicals with similar toxicological profiles so that they can be assessed in groups rather than individually. We have previously developed a text mining (TM)-based tool that can automatically identify the mode of action (MOA) of a carcinogen based on the scientific evidence in literature, and it can measure the MOA similarity between chemicals on the basis of their literature profiles (Korhonen et al., 2009, 2012). A new version of the tool (2.0) was recently released and here we apply this tool for the first time to investigate and identify meaningful groups of chemicals for risk assessment. We used published literature on polychlorinated biphenyls (PCBs)-persistent, widely spread toxic organic compounds comprising of 209 different congeners. Although chemically similar, these compounds are heterogeneous in terms of MOA. We show that our TM tool, when applied to 1648 PubMed abstracts, produces a MOA profile for a subgroup of dioxin-like PCBs (DL-PCBs) which differs clearly from that for the rest of PCBs. This suggests that the tool could be used to effectively identify homogenous groups of chemicals and, when integrated in real-life risk assessment, could help and significantly improve the efficiency of the process.
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden. Electronic address: imran.ali@ki.se.
authors.forename: Imran
authors.initials: I
authors.lastname: Ali
authors.affiliation: Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DA, UK.
authors.forename: Yufan
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden.
authors.forename: Ilona
authors.initials: I
authors.lastname: Silins
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Högberg
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden.
authors.forename: Ulla
authors.initials: U
authors.lastname: Stenius
authors.affiliation: Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge CB3 9DA, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Korhonen
mesh_terms: D000818:Animals; D002273:Carcinogens / Q000633:toxicity*; D016022:Case-Control Studies; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004785:Environmental Pollutants / Q000633:toxicity*; D006801:Humans; D009930:Organic Chemicals / Q000633:toxicity; D011078:Polychlorinated Biphenyls / Q000633:toxicity*; D018570:Risk Assessment / Q000379:methods*
chemical_list: D002273:Carcinogens; D004785:Environmental Pollutants; D009930:Organic Chemicals; D011078:Polychlorinated Biphenyls
keywords: CRAB 2.0; Chemical risk assessment; Classification of literature; Mode of action; Polychlorinated biphenyls; Text-mining