Munging for deeper meaning in nanotoxicologic studies with select model-driven discovery, cleaner nanoontolytics, and tightly-targeted in silico text queries NANOTOX MDD™ LaVerne Poussaint*1, Devashish Tyagi2 Plutonic Research & Knowledge Teams Intl. [PRAKTI], DeepMed Library division, USA 2 Indian Institute of Technology, Department of Computer Science and Engineering, India *1poussaint [at] deepmed.net; 2devashish-cs [at] student.iitd.ac.in 1
DATA DETRITUS REDUCTION
DIRTY DATA & DENOTATA
HOMING IN ON THE HIDDEN THIRD
http://www.wordle.net/show/wrdl/3917389/NanoTox_MDD
http://www.wordle.net/show/wrdl/3917257/NANOTOX_MDD
http://www.wordle.net/show/wrdl/3917472/NANOTOX_MDD3
Unlike other emergent but comparatively better-established sub-fields within the nanosciences, the nascent disciplines of nano- toxicology and pathology are dense harvesting fields of ‘unknown unknowns’, yielding tangled thickets of lurking variables and complex, uncontrolled-for confounding factors.1 The roots of data detritus in nanorisk studies stem from malformed morphologies propagated through labs, germinating throughout research repositories, requiring anatomic, systemic weeding-out of invasively procreative, inaptly-introduced terminologies. So deeply embedded throughout library databases and institutional depositories is ill-structured nanotox nomenclature that its semantic offshoots choke off and threaten organic development of nano-’s broader landscape.2 We propose a knowledge-discovery aide devised to resolve noisy nano-ontolytics within computational nanotoxicology.
Computational NanoToxicology pushes back investigative frontiers with the quickening power of predictive modelling.3 Quantitative in silico techniques take verifiable, virtual leaps between and among the lacunae of exploratory, resource-intensive basic research, extensive examination required of applied clinical trial endpoints, and laboriously lengthy deliberations on minimum nanosafety standardization, revealing through model-driven discovery the clearest way forward amidst dense and dirty data. With a view towards semantic clarity engendering speed, precision, and harmonization4, we devised a single-access-point mechanism [to be hosted by the medical and scientific literature publisher, Elsevier] rendered on the SciVerse Application platform utilizing its two major components: SciVerse core Application Framework and SciVerse Content Application Programming Interface [API].
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As a nano-knowledge discovery interface, NanoTox MDD will - with improved tweaking and testing - meet urgent and unmet needs within nanotoxicology and nanopathology subfields. View the alpha demonstration via Elsevier’s Apps for Science Software Submission Gallery: http://appsforscience.com/submissions/4143-nanotox-mdd 1
The semantic rigours required by the truly translational knowledge domains of nanotoxicology and nanopathology are not being met by existing bio-informatics metadata strategies, 5. These highly intuitive, deeply complex interdisciplinary fields produce pragmatic search difficulties not overcome by Google Scholar algorithms. Supplying the human algorithm, we parsed nano-obfuscata which impede precise interpretation of engineered nanoparticle risk assessments,, hamper nanobiohazard management evaluations, and impair nanomaterials health governance. With refined filter factoring, delimited descriptors for nine hierarchical and hybrid models, and drilldowns into DeepMed’s hyper-linked database [extending well beyond SciVerse silos] we aimed towards: more elegant workflows sequenced at inception of experimentation ; swifter state-of-the-science publication appraisals through elimination of literary search returns which litter the nano-information landscape; machine– learning facilitation; emphasis on revolutionary methodologies [ie, nanoparticle-protein corona interactions and uncommon metrologic isolations of micro/macro dimensions We discovered inherently grey lit repetitions, inherited definition deviations, disjointed search parameters, ambiguous patterning resulting from inconsistent abbreviations and other data distortions wherein lurking variables hide.
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Nanostructure Toxicity Relationships: Outcome of the COST Exploratory Workshop of Relationships: Outcome of the COST Exploratory Workshop of April 2011 Booklet; Netherlands. 2 Thomas DG, Klaessig F, Harper SL, Fritts M, Hoover MD, Gaheen S, Stokes TH, Reznik-Zellen R, Freund ET, Klemm JD, Paik DS, Baker NA. 2011. Informatics and standards for nanomedicine technology. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, 3: 511–532. DOI: 10.1002/wnan.152 3 Tran L, Navas Antón JM. 2009 Second Quarter. Nanotoxicology and Engineered NanoParticle Risk Assessment: Industrial Hygiene. Seguridad y Medio Ambiente; (29) 114: 1-45. 4 International Alliance for NanoEHS Harmonization http://www.nanoehsalliance.org/sections/Protocols Accessed: 29 Aug 2011. 5 Katayama T, Wilkinson MD, Vos R, Kawashima T, Kawashima S, Nakao M, Yamamoto Y, Chun HW et al. 2011. The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications. Journal of Biomedical Semantics 2:4. DOI: 10.1186/2041-1480-2-4