Juniper Publishers-Biomedical Engineering Journals

  Abstract Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel feature detection and engineering machine-learning framework is presented to address this need. First, the  Rip Curl  process is applied which generates a set of 10 additional features. Second, we rank all features including the  Rip Curl  features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are used in model building. This process creates for more expressive features which are captured in models with an eye towards the model learning from increasing sample amount and the accuracy/time results. The performance of the proposed  Rip Curl  classification framework was tested on omentum cancer data.  Rip Curl  outperformed other more sophisticated

  Abstract The ”whole-cell” simulation of cell metabolic processes under considering a variable-volume modelling framework has been reviewed to prove their advantages when building-up modular model structures of simplified form that can reproduce complex protein syntheses inside cells. The more realistic “whole-cell-variable-volume” (VVWC) approach is reviewed when developing modular kinetic representations of the homeostatic gene expression regulatory modules (GERM) that control the protein synthesis and homeostasis of metabolic processes. The paper review the general concepts of the VVWC modelling, while the cited literature includes past and current experience with GERM linking rules in order to point-out how optimized globally efficient kinetic models for the genetic regulatory circuits (GRC) can be obtained to reproduce experimental observations. Based on quantitative regulatory indices evaluated vs. simulated dynamic and stationary environmental perturbations, the reviewed litera