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Journal Article
A. Sirbu, Ruskin, H. J., and Crane, M., “Integrating heterogeneous gene expression data for gene regulatory network modelling.”, Theory Biosci, vol. 131, pp. 95-102, 2012.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, pp. 1–19, 2017.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., “NDlib: a python library to model and analyze diffusion processes over complex networks”, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
A. Sirbu, Loreto, V., Servedio, V. D. P., and Tria, F., “Opinion dynamics with disagreement and modulated information”, Journal of Statistical Physics, pp. 1–20, 2013.
A. Sirbu, Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V. D. P., Stumme, G., Theunis, J., Tria, F., and Van den Bossche, J., “Participatory Patterns in an International Air Quality Monitoring Initiative.”, PLoS One, vol. 10, p. e0136763, 2015.
A. Sirbu, Giannotti, F., Pedreschi, D., and Kertész, J., “Public opinion and Algorithmic bias”, ERCIM News, 2019.
A. Sirbu, Ruskin, H. J., and Crane, M., “Regulatory network modelling: Correlation for structure and parameter optimisation”, Proceedings of The IASTED Technology Conferences (International Conference on Computational Bioscience), Cambridge, Massachusetts, pp. 3473–3481, 2010.
A. Sirbu, Kerr, G., Crane, M., and Ruskin, H. J., “RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.”, PLoS One, vol. 7, p. e50986, 2012.
A. Sirbu and Babaoglu, O., “Towards operator-less data centers through data-driven, predictive, proactive autonomics”, Cluster Computing, pp. 1–14, 2016.
D. Marbach, Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., Aderhold, A., Stolovitzky, G., Bonneau, R., Chen, Y., Cordero, F., Crane, M., Dondelinger, F., Drton, M., Esposito, R., Foygel, R., De La Fuente, A., Gertheiss, J., Geurts, P., Greenfield, A., Grzegorczyk, M., Haury, A. - C., Holmes, B., Hothorn, T., Husmeier, D., Huynh-Thu, V. A., Irrthum, A., Karlebach, G., Lebre, S., De Leo, V., Madar, A., Mani, S., Mordelet, F., Ostrer, H., Ouyang, Z., Pandya, R., Petri, T., Pinna, A., Poultney, C. S., Rezny, S., Ruskin, H. J., Saeys, Y., Shamir, R., Sirbu, A., Song, M., Soranzo, N., Statnikov, A., Vega, N. M., Vera-Licona, P., Vert, J. - P., Visconti, A., Wang, H., Wehenkel, L., Windhager, L., Zhang, Y., and Zimmer, R., “Wisdom of crowds for robust gene network inference”, Nature Methods, vol. 9, pp. 796-804, 2012.

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