Data from in vitro differential gene expression analysis studies were gathered from peer-reviewed scientific literature. The studies gathered had a considerably variety of different human cell models including both primary cells and immortalized cell lines which exhibit varying responses.

For each of the studies reviewed, the combination of experimental exposure conditions, in vitro characteristics, such as cell line, cell type and tissue, physicochemical properties and outcomes are recorded separately in a datasheet, resulting in many extractions per study. If, for instance, another dose is administered during an experiment with all the other conditions unchanged, this was inserted as new instance within the dataset. Biological effects comprising a number of cellular responses were extracted and used as the outcome of interest to be simulated by the BN model. Effect occurrence was expressed in binary manner (Triggered, No effect).

(Shim, Paik, Nguyen, Lee and et al. 2012), (Zhang, Wang, Zou and et al. 2015), (AshaRani, Sethu, Lim, Balaji, Valiyaveettil and Hande, 2012), (Böhmert, Niemann, Lichtenstein, Juling and Lampen, 2015), (Bouwmeester, Poortman, Peters and et al., 2011), (Eom, Chatterjee, Lee and Choi, 2014), (Foldbjerg, Irving, Hayashi and et al., 2012), (Kawata, Osawa and Okabe, 2009), (Sahu, Zheng, Yourick, Sprando and Gao, 2015),(van der Zande, Undas, Kramer and et al., 2016),(Xu, Li, Takemura, Hanagata, Wu and Chou, 2012), (Bajak, Fabbri, Ponti and al., 2015), (Grzincic, Yang, Drnevich, Falagan-Lotsch and Murphy, 2015), (Yang, Qu and Lu, 2010), (Kim, Schulz, Swantek, Kunstman, Malim and Wolinsky, 2012), (Hanagata, Zhuang, Connolly, Li, Ogawa and Xu, 2011), (Jang, Oh, Yang and Cho, 2016), (Peng, Barczak, Barbeau and et al., 2010), (Tuomela, Autio, Buerki-Thurnherr and et al., 2013), (Fujita, Horie, Kato and et al., 2009), (Tilton, Karin, Tolic, Xie, Lai, Hamilton, Waters, Holian, Witzmann and Orr, 2014), (Moos, Olszewski, Honeggar, Cassidy, Leachman, Woessner, Cutler and Veranth, 2011), (Hanagata, Xu, Takemura and Zhuang, 2010), (Lee, Pie, Kim, Lee, Son and Kim, 2012), (Hussien, Rihn, Eidi and et al., 2013), (Fröhlich, Meindl, Wagner, Leitinger and Roblegg, 2014)

Researchers should cite this work as follows:

• Irini Furxhi (2019), "Literature transcriptomics review and data of Nanoparticle Induced Cellular Outcomes," https://nanohub.org/resources/30099.

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1. Bayesian
2. Bayesian networks
3. machine learning
4. nanoparticles
5. NanoToxicology
6. Transcriptomics