Thermodynamically inspired classifier for molecular phenotypes of health and disease

High-throughput molecular phenotyping (e.g., transcriptomics, proteomics, DNA sequencing, epigenetics, etc.) has impacted cancer research, enabling the subtyping of disease states and the discovery of actionable molecular signatures and biomarkers (1, 2). While the use of these technologies has proved more challenging than initially anticipated (3), the important role for molecular phenotyping in the health sciences is broadly appreciated. The Cancer Genome Atlas (4), assembled to integrate multidimensional molecular omic data through systems approaches, is one indicator. The development of classifiers for molecular phenotypes that enable clinically useful stratification of patients into treatment groups remains a critical challenge to overcome. Technical and biological noise, patient heterogeneity, sample contamination, inconsistent sample processing, and changing data-collection platforms all make this a difficult task. Combining classifiers to increase sensitivity (5) has only proved incrementally useful. Thus, informatic approaches that can deal robustly with noisy and heterogeneous molecular phenotyping data remain crucial to develop. The report in PNAS by Zadran et al. (6) highlights a new twist in which an established analytical approach drawn from the physical sciences is applied to the analysis of molecular phenotypes. i) All transcripts contribute to the description of a cellular state, but they do so with a thermodynamic weight that is proportional to their abundance. The principled assignment of weights distinguishes surprisal analysis from analytical methods that use fold-change and cut-offs. The latter bias analyses toward the …