Machine Learning for Hypothesis Generation in Biology and Medicine: Exploring the latent space of neuroscience and developmental bioelectricity

Artificial intelligence is a powerful tool that could be deployed to accelerate the scientific enterprise. Here we address a major unmet need: use of existing scientific literature to generate novel hypotheses. We use a deep symmetry between the fields of neuroscience and developmental bioelectricity to test a tool, FieldSHIFT, that generates scientific text from existing published studies. We show how this system helps human scientists explore a latent space of papers that could exist, which provides a rich field of suggested future research. We then test a key prediction of this model using bioinformatics, showing a surprising conservation of molecular mechanisms involved in cognitive behavior and developmental morphogenesis. By allowing scientists to rapidly explore symmetries and meta-parameters that exist in a corpus of scientific papers, we show how machine learning can potentiate human creativity and assist with one of the most interesting and crucial aspects of research: identifying insights from data and generating potential candidates for research agendas.