title: Searching for interpretable rules for disease mutations: a simulated annealing bump hunting strategy.
abstract: BACKGROUND
  Understanding how amino acid substitutions affect protein functions is critical for the study of proteins and their implications in diseases. Although methods have been developed for predicting potential effects of amino acid substitutions using sequence, three-dimensional structural, and evolutionary properties of proteins, the applications are limited by the complication of the features and the availability of protein structural information. Another limitation is that the prediction results are hard to be interpreted with physicochemical principles and biological knowledge.
  
  
  RESULTS
  To overcome these limitations, we proposed a novel feature set using physicochemical properties of amino acids, evolutionary profiles of proteins, and protein sequence information. We applied the support vector machine and the random forest with the feature set to experimental amino acid substitutions occurring in the E. coli lac repressor and the bacteriophage T4 lysozyme, as well as to annotated amino acid substitutions occurring in a wide range of human proteins. The results showed that the proposed feature set was superior to the existing ones. To explore physicochemical principles behind amino acid substitutions, we designed a simulated annealing bump hunting strategy to automatically extract interpretable rules for amino acid substitutions. We applied the strategy to annotated human amino acid substitutions and successfully extracted several rules which were either consistent with current biological knowledge or providing new insights for the understanding of amino acid substitutions. When applied to unclassified data, these rules could cover a large portion of samples, and most of the covered samples showed good agreement with predictions made by either the support vector machine or the random forest.
  
  
  CONCLUSIONS
  The prediction methods using the proposed feature set can achieve larger AUC (the area under the ROC curve), smaller BER (the balanced error rate), and larger MCC (the Matthews' correlation coefficient) than those using the published feature sets, suggesting that our feature set is superior to the existing ones. The rules extracted by the simulated annealing bump hunting strategy have comparable coverage and accuracy but much better interpretability as those extracted by the patient rule induction method (PRIM), revealing that the strategy is more effective in inducing interpretable rules.
authors.affiliation: Molecular and Computational Biology, University of Southern California, MCB201, 1050 Childs way, Los Angeles, CA 90089-2910, USA. ruijiang@usc.edu
authors.forename: Rui
authors.initials: R
authors.lastname: Jiang
authors.forename: Hua
authors.initials: H
authors.lastname: Yang
authors.forename: Fengzhu
authors.initials: F
authors.lastname: Sun
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
mesh_terms: D000595:Amino Acid Sequence; D019943:Amino Acid Substitution / Q000235:genetics; D017122:Bacteriophage T4 / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004927:Escherichia coli Infections / Q000235:genetics / Q000382:microbiology; D029968:Escherichia coli Proteins / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D008969:Molecular Sequence Data; D009154:Mutation; D011237:Predictive Value of Tests
chemical_list: D029968:Escherichia coli Proteins