Controlling Informative Features for Improved Accuracy and Faster Predictions in Omentum Cancer Models

 

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 classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

Introduction

In recent years, the dawn of technologies like microarrays, proteomics, and next-generation sequencing has transformed life science. The data from these experimental approaches deliver a comprehensive depiction of the complexity of biological systems at different levels. A challenge within the “-omics” data strata is in finding the small amount of information that is relevant to a particular question, such as biomarkers that can accurately classify phenotypic outcomes [1]. This is certainly true in the fold of peritoneum connecting the stomach with other abdominal organs known as the omentum. Numerous machine learning techniques and methods have been proposed to identify biomarkers that accurately classify these outcomes by learning the elusive pattern latent in the data. To date, there have been three categories that assist in biomarker selection and phenotypic classification:

  1. Filters
  2. Wrappers
  3. Embedding

In practice, time-to-prediction and accuracy of prediction matter a great deal.


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