Machine Learning is Disrupting Life Science Research – For Good

Big data is getting bigger by the minute. Preclinical researchers are focused on developing new hypotheses and ideas that can eventually translate into testing and deployment, which includes gathering an enormous amount of data, understanding and connecting the dots of different pathways, and coming to meaningful conclusions. There is also increasing demand, particularly in oncology, for better prognostic tests and companion diagnostics to inform treatment. Researchers are thus tasked with analyzing overwhelming amounts of data to identify biomarkers and develop robust assays. This is essentially like searching for a needle in a haystack, and is an incredibly time-intensive and challenging process.

Read the source article at Bioscience Technology