A Novel Profile Biomarker Diagnosis for Mass Spectral Proteomics

Abstract

Mass spectrometry based proteomics technologies have allowed for a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, they face acute challenges from a data reproducibility standpoint, in that no two independent studies have been found to produce the same proteomic patterns. Such reproducibility issues cause the identified biomarker patterns to lose repeatability and prevent real clinical usage. In this work, we propose a profile biomarker approach to overcome this problem from a machine-learning viewpoint by developing a novel derivative component analysis (DCA). As an implicit feature selection algorithm, derivative component analysis enables the separation of true signals from red herrings by capturing subtle data behaviors and removing system noises from a proteomic profile. We further demonstrate its advantages in disease diagnosis by viewing input data as a profile biomarker. The results from our profile biomarker diagnosis suggest an effective solution to overcoming proteomics data's reproducibility problem, present an alternative method for biomarker discovery in proteomics, and provide a good candidate for clinical proteomic diagnosis.

Analyzing Support Vector Machine Overfitting on Microarray Data

“…However, we would like to further investigate potential overfitting overcome algorithms by integrating improved NMF algorithms in support vector machine classifications. In the future work, we plan to employ sparse coding techniques or nonnegative parameter argument approaches to improve the generality and robustness of data representation in NMF and decrease the complexities of NMF by exploring embedding wavelet based multi-resolution techniques in NMF [16].…”

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

“…have been widely for various applications including identification of breast cancer biomarkers [92], diagnosis biomarker of Parkinson disorders [93], subcellular locations of proteins [94,95], the prediction of protein functions on the basis of protein structures [96,97], the annotation of mutations [98,99]. For example, Han proposed a machine learning based derivative component analysis method to select implicit feature by capturing subtle data behaviors and removing system noises from a proteomic profile to overcome the reproducibility problem for biomarker discovery in proteomics [100]. Another interesting study by Hoshida et al [101] combined eight independent cohorts of gene expression profiles to reveal the subclass of HCC and their related pathways using unsupervised machine learning methods.…”