The past few years have seen several flashy demonstrations of how artificial intelligence (AI) algorithms may transform biomedical research, particularly with respect to drug discovery.
The past few years have seen several flashy demonstrations of how artificial intelligence (AI) algorithms may transform biomedical research, particularly with respect to drug discovery.
Metabolomics—often referred to as the youngest of the omics—provides key insight into phenotype. However, bulk metabolomics requires the homogenization of the sample and is thus unable to discern metabolic differences at a cellular level.
Zachary Pitluk, Ph.D., Vice President of Life Sciences and Healthcare at Paradigm4, highlights how real insight comes, not from data collection, but from intelligent data curation, computation, and application.
Single-cell technologies have positioned themselves at the forefront of biomedical research. These platforms allow researchers to bypass the uncertainties of bulk data and instead interrogate biological systems at a level of detail that was previously unreachable. But the value of these systems risks being impaired by bottlenecks in data analysis