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Machine learning techniques predict and characterize toxicity between different multi-walled carbon nanotubes

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Machine learning techniques predict and characterize toxicity between different multi-walled carbon nanotubes

Content Provider	ACM Digital Library
Author	Yanamala, Naveena Bishop, Lindsey M. Zeidler-Erdely, Patti C. Erdely, Aaron D. Kodali, Vamsi K.
Abstract	Multi-walled carbon nanotubes (MWCNT) are a diverse class of engineered nanomaterials known to induce pulmonary toxicity. It is unclear if the mechanisms contributing to the adverse effects are similar or unique when comparing different MWCNT. We hypothesize that machine learning techniques applied to toxicity outcomes will distinguish between convergent and divergent molecular responses. Towards this goal, a panel of proteins measured by multi-plex technology in bronchoalveolar lavage collected 1, 28, and 84 d post-exposure from mice exposed to two different as-produced MWCNT, their polymer coated counterparts, or a well-studied reference material, MWCNT-7, were analyzed using association rule mining (ARM) and support vector machine (SVM) techniques. The main objective was to take advantage of both knowledge represented by class association rules and the power of SVM to identify a small number of highly predictive markers (4 to 7 protein panels out of a total 63 proteins) that can distinguish between exposed and unexposed animals. Using these approaches, we were able to reliably (78% -- 97%) identify a small subset of proteins for each exposure that clearly distinguishes effect of exposure. One mediator in particular, MDC/CCL22, was associated with all exposures. MDC protein levels have been shown to be very sensitive in determining MWCNT exposure and predict declining lung function in humans following particulate exposure. Additional mediators (e.g., VEGF-A, MIP1b) exhibited similarity while others (e.g., LIF, MMP-9, VCAM1, PAI-1) distinguished one exposure from another. In vivo toxicity studies indicate that polymer coating the MWCNT for downstream applications can affect pulmonary toxicity. The machine learning techniques clearly distinguished the polymer coated MWCNT from the as-produced counterpart with up to 96% reliability, depending on particle type and exposure duration. The identified patterns in altered lavage proteins may serve as valuable markers to guide detection of exposure to MWCNT and offer hypothesis for future study design. The approaches presented in this study could enable comparison not only within a class of engineered nanomaterials but between various classes of nanomaterials.
Starting Page	517
Ending Page	517
Page Count	1
File Format	PDF
ISBN	9781450342254
DOI	10.1145/2975167.2985664
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Nanotoxicity models Association rule mining Mwcnt toxicity Support vector machine
Content Type	Text
Resource Type	Article

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