Deep Learning is a set of technologies which use huge amount of data to predict behaviors thanks to hidden (or not) signals inside. In this article for example, we see how a collaboration between Google scientists and Steve Finkbeiner’s use brain cells pictures to automatize “live / dead” classification, and many others ways to use it ! But, deep learning is not the “Holy Grail” of pictures interpretation, because algorithms has to be trained with many heterogeneous data, and it use also different methods. The challenge for biologists is to understand results and to link it with biology !

In HCS Pharma we currently produce huge amount of pictures and data and we are working on machine learning and deep learning technologies to help interpretation. First results are encouraging but we need time to propose a robust methodology to our customers. Stay connected !

More than 440 articles on the bioRxiv preprint server discuss deep learning; PubMed lists more than 700 references in 2017. And the tools are on the cusp of becoming widely available to biologists and clinical researchers. But researchers face challenges in understanding just what these algorithms are doing, and ensuring that they don’t lead users astray.

Source: Deep learning for biology

Grégory MAUBON

Grégory MAUBON is Chief Data Officer and digital coordinator at HCS Pharma, a biotech startup focused in high content screening and complex diseases. He manages IT missions and leads digital usages linked to company needs. He is also a Augmented Reality Evangelist (presenter and lecturer) since 2008, where he created and founded in 2010 RA'pro (the augmented reality promotion association). He helped many companies (in several domains) to define precisely their augmented reality needs and supported them in the implementation.


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