In this post, we examine applications of deep learning to three key biomedical problems: patient classification, fundamental biological processes, and treatment of patients. The objective is to predict whether deep learning will transform these tasks.
The post is based on the very comprehensive paper “Opportunities and obstacles for deep learning in biology and medicine”.
The paper places a high bar i.e. on the lines of Andy Grove’s inflection point to refer to a change in technologies or environment that requires a business to be fundamentally reshaped.
The three classes of applications are described as follows:
Disease and patient categorization: the accurate classification of diseases and disease subtypes. In oncology, current "gold standard" approaches include histology, which requires interpretation by experts, or assessment of molecular markers such as cell surface receptors or gene expression.
Fundamental biological study: application of deep learning to fundamental biological questions using methods based on leveraging large amounts.
Treatment of patients: new methods to recommend patient treatments, predict treatment outcomes, and guide the development of new therapies.
Within these, areas where deep learning plays a part for biology and medicine are
Deep learning and patient categorization
Deep learning to study the fundamental biological processes underlying human disease
The impact of deep learning in treating disease and developing new treatments
There are a number of areas that impact deep learning in biology and medicine
I found two particularly interesting aspects: interpretability and data limitations. As per the paper:
The authors conclude that deep learning has yet to revolutionize or definitively resolve any of these problems, but that even when improvement over a previous baseline has been modest, there are signs that deep learning methods may speed or aid human investigation.
This is a comprehensive paper which I recommend
The paper also has a github repository
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