The High-Throughput Analyses Era: Are We Ready for the Data Struggle?
AbstractRecent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine. View Full-Text
Share & Cite This Article
D’Argenio, V. The High-Throughput Analyses Era: Are We Ready for the Data Struggle? High-Throughput 2018, 7, 8.
D’Argenio V. The High-Throughput Analyses Era: Are We Ready for the Data Struggle? High-Throughput. 2018; 7(1):8.Chicago/Turabian Style
D’Argenio, Valeria. 2018. "The High-Throughput Analyses Era: Are We Ready for the Data Struggle?" High-Throughput 7, no. 1: 8.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.