There are many disciplines that have contributed to how one model's risk and return. Physics introduced Brownian motion and RMT. Machine learning has helped to solve complex portfolio construction problems, operations research has contributed to market making & risk management, and aerospace engineering has contributed the Kalman filter.
I am looking for papers that use methods traditionally applied to bioinformatics and genetics to model the behavior of equities. For example, epidemiology makes extensive use of canonical regression -- Blackburn et al. apply canonical regression to tease out the co-movement of equities here.
I am not looking for links to neural network, SVM, or other machine learning papers. I am looking for creative and novel extensions of methods used traditionally in the bioinformatics and genetics space that are applied to generate alpha or risk models in U.S. equities?
For example, bioinformatics has developed a variety of greedy search alorithms that identify which types of traits give rise to certain phenotypes. Is there a paper that applies some of the algorithms from the bioinformatics domain to U.S. equities where traits might be financial statement variables and phenotypes might be return outcomes?
I know Carlos Carvalho (U. Chicago) has done both innovative work in the field of equities and also genetics and recall he had some research along these lines.