Advanced algorithms such as Principal Component Analysis and BiClustering provide researchers with ways of finding the needle in the haystack of big datasets, saving time and money. Today, data is accumulating at tremendous rates. The challenge is to process it all in a meaningful way. Machine learning, is essentially getting computers to act without being programmed. Before today’s age of big data, data science was often done by trial-and-error analysis, with all parameters specified by the programmer. Today however, data sets are now very large and varied, and this approach is no longer viable. Machine learning empowers the discovery of patterns within massive data sets patterns that cannot be seen by human eyes and can deliver value from all types of big data.These patterns can then be investigated by researchers,allowing them to quickly move past this first crucial step and move on to identifying meaningful biological interactions. The more data is given to a machine-learning tool, the better it can identify patterns leading to higher quality insights.