Math Modeling Meets Machine Learning

Math Modeling Meets Machine Learning

Focused on facilitating the iterative refinement of ground truth in the bio-sciences through smarter collaboration and co-opetitions, our co-opetitions are within the space of Systems Biology, and more importantly, Structural Systems Biology.

The core objective of these co-opetitions are to merge and integrate current tools and techniques in traditional Mathematical Modeling with newer Machine Learning approaches for developing better Structural Systems Biology Models. Sub-tasks will include but are not limited to, data augmentation, learning of biologically consistent constraints, better interpretability of models, and generally, more robust end to end hybrid models. 

Co-opetitions are designed based on recent advances in Machine Learning, coupled with the work on Constraint-Based Modeling at Dr. Bernhard Palsson’s Lab at UCSD, and work on Data-Driven Sparse Identification of Nonlinear Dynamics Of Biological Networks at Steven Brunton’s lab at University of Washington.

What has now become an intensely competitive territory will inevitably need to be redirected into a more collaborative space for the continued engagement of all the brilliant minds coming into and already in the field. This includes tackling certain problems such as assembly order of multimeric complexes

Embed, encode, attend & predict.