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Computational Biology with Deep Learning

Karishma Dubey


Technological advances in genomics and imaging have resulted in an explosion of molecular and cellular profiling data from large numbers of samples. Conventional analytic methodologies are being tested by the increasing expansion of biological data dimension and acquisition rate. Modern machine learning technologies, such as deep learning, promise to make accurate predictions and identify underlying structure in very huge data sets. In this study, we look at how regulatory genomics and cellular imaging can benefit from this new breed of analysis tools. We give an overview of what deep learning is and how it may be used to obtain biological insights in different situations. We emphasise probable hazards and restrictions to guide you, in addition to presenting real applications and providing recommendations for practical use. We point out potential problems and limitations to help computational biologists decide when and how to use this new technique. We emphasise potential dangers and limitations to assist computational biologists when and how to make the most of this new tool, in addition to showing specific applications and providing practical recommendations.


Cellular imaging, Computational biology, Deep learning, Machine learning, Regulatory genomics

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