Preface

In the last decade, the scientific community assisted in a real revolution determined by the development of technologies that caused a rapid increase in the amount of information usable by researchers. While traditional analytic approaches were based on the study of single molecules, novel technologies permitted a characterization of entire pools of specific biomolecules. Consequently, the term “Omic Sciences” was coined, highlighting the global vision that derives from this kind of study. Among them, genomics, transcriptomics, proteomics and metabolomics represent the most innovative branches that belong to the omic universe. In contrast to the relevance of these methodologies, the common disadvantage of the big amount of data generated and has arisen, hence, its management. Therefore, in order to minimize the “big data” complexity, there was a huge progress of bioinformatic areas, trying to analyse data faster and more accurately. Nowadays, computational sciences are continuously developed, and several tools, based on bioinformatic and biostatistical analysis pipelines, are programmed. Based on this consideration, I think that novel insights in omics experimental procedures and, predominantly, in new strategies for data analysis could provide an interesting and exploitable topic for many researchers. So, the idea for this book series is to realize an integrated approach between all omic sciences, exploiting innovative bioinformatics and biostatistical methodologies able to unveil hidden sides of these scientific areas. This first volume of the proposed book series would face the application of innovative analytic pipelines to obtain the most useful and translational results from genomics and transcriptomics data, with the fundamental support of machine learning algorithms and innovative biostatistical models. Such procedures will be applied to real data coming from human sample analyses, ranging from biopsies to cell cultures. I think that the holistic approach of previously discussed sciences could permit us to advance towards new scenarios, finally trying to see the “big data” as a precious resource rather than a real problem to be faced.