During the past decade, rapid advances in high-throughput technologies have brought unprecedented opportunities for the large-scale analysis of the nicotine addiction-related genes/proteins, leading to a rapid generation of large-scale nicotine addiction-related data. These datasets are often heterogeneous and multi-dimensional, which makes integrating and arranging such datasets to ascertain the key molecular mechanisms and to transform the data into meaningful biological phenomenon a major task and challenge. To meet the demand, pathway and network-based analyses have become an important and powerful approach to elucidate the biological implications underlying complex diseases [14–16]. Such a systems biology approach could be pivotal for better understanding of mental disorder at the molecular level [17]. Thus, a comprehensive analysis of the nicotine addiction-related candidate genes within a systematic framework may provide us important insights on the molecular mechanisms underlying nicotine addiction.
