A fuzzy rule based approach to identify biomarkers for diagnostic classification of cancers

Abstract

An important problem for doctors is to identify a small set of useful biomarkers (not all related genes) that can discriminate between different subgroups of cancers which appear similar in routine histology. Here we propose a method for simultaneous feature/gene selection and rule generation for the same problem. Since the feature selection method is integrated into the rule base tuning, it can account for possible subtle nonlinear interaction between features as well as that between features and the tool, and hence can identify a useful set of features for the task at hand. We applied our method to find biomarkers for a group of four childhood cancers that is collectively known as small round blue cell tumors. For this data set first we have used a neural network to reduce the dimension of the data and then applied our method to find biomarkers and rules. Our system could find only eight genes including a novel gene that can do the diagnostic prediction task with a high accuracy. The system can be extended to non-classification applications also.

Exhaled human breath analysis in active pulmonary tuberculosis diagnostics by comprehensive gas chromatography-mass spectrometry and chemometric techniques

“…Practically, feature selection to a manageable size is necessary in order to translate biomarkers to a handheld or benchtop system in a clinic or diagnostic laboratory. [40]. RF algorithms generate many classification trees, using randomly selected subsamples of both features and data points.…”

Fuzzy Rule-Based Approaches to Dimensionality Reduction

“…Many results by each of these methods can be found in Pal et al [2002], Pal [2007] and in Pal and Saha [2008]. For structure-preserving dimensionality reduction we consider a few data sets whose geometric structures are known so that we can assess the quality of the results.…”

“…The problem of dimensionality reduction can be solved using many computational approaches such as neural networks, fuzzy logic, and evolutionary computing [2007,1997,2006,2004]. Here we shall restrict ourselves to only fuzzy rule based approaches [2002].…”

“…Further details about the classifier systems can be found in Pal [2007], while the details for the TS systems can be obtained from Pal and Saha [2008]. If a feature is important (i.e., if it can reduce the system training error), then the corresponding feature modulating gate will be opened faster, while if a feature is derogatory (i.e., it cannot reduce the error), the associated gate will not open.…”