Multivariate Analyses and the Bridging of Biology's ‘Math‐Gap’

Abstract

This article shows how multivariate data modeling and multivariate metamodeling bridge the math‐gap in many sciences, e.g. in the biosciences. Biomedical science is constituted by two traditionally diverging lines of research, one being explorative, inductive, and holistic and the other being confirmative, deductive, and reductionistic. We argue in this article that both cultures are in dire need of more cross‐disciplinarity and openness to the knowledge and opportunities that lie on the other side of what we here call the ‘Math‐Gap in Bioscience’: The former tradition, which we choose to call ‘The House of Bio’, needs to expand to more powerful data‐driven statistical modeling and assessments in order to make sense out of their deluge of measurement data. The latter, which we here call ‘The House of Math’, needs to develop more and better mechanistic models to make possible the quantitative combination and effective utilization of today's enormous growth in biomedical knowledge and data. However, the use of mathematics and statistics in biomedical fields is hampered by lack of contact, curiosity, and respect between these two research cultures. To bridge this ‘Math‐Gap’ between them, we here propose a simple but powerful toolbox: soft multivariate modeling based on graphically interpreted, cross‐validated bilinear analysis. Nine different examples of data modeling and metamodeling show how this is successfully used in widely different fields of biomedical research – and from both research traditions.