Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery.

摘要: The tremendous expansion of data analytics and public private big datasets presents an important opportunity for pre-clinical drug discovery development. In the field life sciences, growth genetic, genomic, transcriptomic proteomic is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, speed. Yet far too many researchers tend to underestimate challenges consequences involving integrity quality standards. Given effect on scientific interpretation, these issues have significant implications during preclinical We describe standardized approaches maximizing utility publicly available or privately generated biological address some common pitfalls. also discuss increasing interest integrate interpret cross-platform data. Principles outlined here should serve useful broad guide existing analytical practices pipelines tool developing additional insights into therapeutics using