Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template

Abstract

MotivationComplex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns.ResultsHere we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches.ConclusionsThe experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias.

Molecular cancer classification using a meta-sample-based regularized robust coding method

“…The analysis flowchart of the meta-sample-based SR method is different from those of traditional model-based and template-based methods (Figure 1 ). The classification models of model-based methods use the training set to predict the labels of test samples, while template-based methods create a template for each subclass using training set and then compare a test sample to the templates in order to determine the label of the test sample [ 3 ]. Although there is similarity between the analysis flowcharts of meta-sample-based SR method and template-based one, there is a major difference (Figure 1 ).…”

“…Since Golub et al . made use of gene expression profiling data, obtained using the DNA microarray technology, to classify acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) [ 2 ], a great number of GEP-based cancer classification methods have been proposed for classifying cancer types or subtypes [ 3 - 6 ]. It has increasingly become clear that common machine learning methods such as support vector machine (SVM) [ 7 , 8 ] and artificial neural networks (ANN) [ 5 , 9 ] may not perform very well because of the curse of dimensionality, as the number of features (genes) is usually much higher than the number of samples in most GEP experiments.…”

Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

“…POC evaluates the similarity level by calculating the peak value of the cross-phase spectrum of two two-dimensional (2D) images. Later, POC was applied to identify the similarity of seismic events, diagnose complex diseases, and detect microseismic events [44][45][46]. The two main properties of the POC are as following: (1) the POC function is not influenced by the intensity difference of data because POC only uses the phase information of data; and (2) the peak position of POC will shift when one image is shifted, and the peak value is invariant [44].…”