Topological Complex Networks Properties for Gene Community Detection Strategy: DRD2 Case Study Anna Monda, Nicola Amoroso, Teresa Maria Altomare Basile, Roberto Bellotti, Alessandro Bertolino, Giuseppe Blasi, Pasquale Di Carlo, Annarita Fanizzi, Marianna La Rocca, Tommaso Maggipinto, Alfonso Monaco, Marco Papalino, Giulio Pergola and Sabina Tangaro Abstract Gene interactions can suitably be modeled as communities through weighted complex networks. However, the problem to efficiently detect these com- munities, eventually gaining biological insight from them, is still an open question. This paper presents a novel data-driven strategy for community detection and tests it on the specific case study of DRD2 gene coding for the D2 dopamine receptor, which plays a prominent role in risk for Schizophrenia. We adopt a combined use of centrality and topological properties to detect an optimal network partition. We find that 21 genes belongs with our target community with probability P ≥ 90 %. The robustness of the partition is assessed with two independent methodologies: (i) fuzzy c-means and (ii) consensus analyses. We use the first one to measure how strong the membership of these genes to the DRD2 community is and the latter to confirm the stability of the detected partition. These results show an interesting reduction (∼80%) of the target community size. Moreover, to allow this validation on different case studies, the proposed methodology is available on an open cloud infrastructure, according to the Software as a Service paradigm (SaaS). A. Monda · A. Bertolino · P. Di Carlo · M. Papalino · G. Pergola Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università Degli Studi di Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy N. Amoroso (B ) · T.M.A. Basile · R. Bellotti · A. Fanizzi · M. La Rocca · T. Maggipinto Dipartimento Interateneo di Fisica “Michelangelo Merlin”, Università Degli Studi di Bari “Aldo Moro”, Via G. Amendola 173, 70126 Bari, Italy e-mail: nicola.amoroso@ba.infn.it N. Amoroso · T.M.A. Basile · R. Bellotti · M. La Rocca · T. Maggipinto · A. Monaco · S. Tangaro Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Via Orabona 4, 70125 Bari, Italy A. Bertolino · G. Blasi Azienda Ospedaliero-Universitaria Consorziale Policlinico, 70124 Bari, Italy A. Bertolino pRED, NORD DTA, F. Hoffman-La Roche Ltd., 4070 Basel, Svizzera © Springer International Publishing AG 2017 G. Mantica et al. (eds.), Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences, Springer Proceedings in Physics 191, DOI 10.1007/978-3-319-47810-4_16 199