Selection of Genetic and Phenotypic Features Associated with Inflammatory Status of Patients on Dialysis Using Relaxed Linear Separability Method

<div><p>Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed linear separability (<i>RLS</i>) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic) clinical data of patients with end-stage renal disease. The <i>RLS</i> method allowed for substantial reduction of the dimensionality through omitting redundant features while maintaining the linear separability of data sets of patients with high and low levels of an inflammatory biomarker. The synergy between genetic and phenotypic features in differentiation between these two subgroups was demonstrated.</p></div

Additional file 1: of Serum micro-rna profiles in patients with autosomal dominant polycystic kidney disease according to hypertension and renal function

List of miRNAs included in the Qiagen miScript miRNA PCR Array Human Serum & Plasma 384HC in Excel format. (XLSX 12.5 KB

AE and CVE - <i>phenotypic</i> space.

<p>The apparent error rate (<i>AE</i>) and the cross-validation error (<i>CVE</i>) in different feature subspaces of the <i>phenotypic</i> space .</p

The cross validation error <i>CVE</i> (mean SD) for different classifiers in the <i>genetic</i> space and their subspaces obtained by using five features selection methods (<i>RLS</i>, <i>ReliefF</i>, <i>CFS-FS</i>, <i>mSVM-RFE</i>, <i>MRMR</i>) and five classifiers (<i>RF</i>, <i>KNN</i>, <i>SVM</i>, <i>NBC</i>, <i>CPL</i>), see Section “Alternative methods for feature selection and classification”.

*<p>ReliefF and MRMR are ranking procedures. The optimal sets of features for these two methods were determined for each classifier separately; the number of features (shown in parentheses) corresponds to the size of the subset of features characterized by the smallest cross validation error for the specific classifier.</p

AE and CVE - <i>genetic</i> space.

<p>The apparent error rate (<i>AE</i>) and the cross-validation error (<i>CVE</i>) in different feature subspaces of the <i>genetic</i> space .</p

AE and CVE - <i>phenotypic</i> and <i>genetic</i> space.

<p>The apparent error rate (<i>AE</i>) and the cross-validation error (<i>CVE</i>) in different feature subspaces of the <i>phenotypic</i> and <i>genetic</i> space .</p

The diagnostic map.

<p>Linear separation of the high <i>CRP</i> from the low <i>CRP</i> patients for the cohort of incident dialysis patients in the optimal feature subspace of the phenotypic and genetic space .</p