Analysis of mass spectral serum profiles for biomarker selection

Ressom, Habtom W.; Varghese, Rency S.; Abdel‐Hamid, Mohamed; Eissa, Sohair A.; Saha, Daniel T.; Goldman, Lenka; Petricoin, Emanuel F.; Conrads, Thomas P.; Veenstra, Timothy D.; Loffredo, Christopher A.; Goldman, Radoslav

doi:10.1093/bioinformatics/bti670

Cited by 80 publications

(77 citation statements)

References 27 publications

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“…We demonstrate our profile biomarker diagnosis' superiority by using five benchmark serum proteomic data sets, which include Cirrhosis, Colorectal, HCC, Ovarian-qaqc and ToxPath data [12,[15][16][17]19]. The benchmark data used in our experiments are heterogeneous data generated from different experiments via different profiling technologies such as MALDI-TOF and SELDI-TOF, and preprocessed by different methods.…”

Section: Resultsmentioning

confidence: 99%

A Novel Profile Biomarker Diagnosis for Mass Spectral Proteomics

Han

2013

Biocomputing 2014

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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.

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Section: Resultsmentioning

confidence: 99%

A Novel Profile Biomarker Diagnosis for Mass Spectral Proteomics

Han

2013

Biocomputing 2014

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“…Ressom et al (70,71) applied PSO-SVM for biomarker selection and sample classification from SELDI-QqTOF and MALDI-TOF spectra in liver cancer studies, with high prediction accuracy. The algorithm combines PSO with SVM to identify the optimal features from a set of potential features.…”

Section: Protein Mass Spectrometrymentioning

confidence: 99%

Classification algorithms for phenotype prediction in genomics and proteomics

Habtom¹

2008

Front Biosci

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This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction. In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively. The selected features are presented to a classifier for phenotype prediction.

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“…These methods can be generally classified as supervised classification methods, such as k-nearest neighborhood (kNN), linear discriminant anayalsis (LDA), neural networks (NN), support vector machines (SVM); [1][2][3] unsupervised classification (clustering) methods, such as hierarchical clustering (HC), self-organizing maps (SOM), principal component analysis (PCA); and their variants, such as particle swarm optimization support vector machines (PSO-SVM), kernel principal component analysis (KPCA) etc. [4][5][6][7] We are particularly interested in the unsupervised molecular pattern discovery algorithms, because they do not need or have prior knowledge about data. They also have potentials to explore the latent structure of data.…”

Section: Introductionmentioning

confidence: 99%

Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification

Han

2007

Computational Systems Bioinformatics

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Cancer molecular pattern efficient discovery is essential in the molecular diagnostics. The characteristics of the gene/protein expression data are challenging traditional unsupervised classification algorithms. In this work, we describe a subspace consensus kernel clustering algorithm based on the projected gradient nonnegative matrix factorization (PG-NMF). The algorithm is a consensus kernel hierarchical clustering (CKHC) method in the subspace generated by the PG-NMF. It integrates convergence-soundness parts-based learning, subspace and kernel space clustering in the microarray and proteomics data classification. We first integrated subspace methods and kernel methods by following our framework of the input space, subspace and kernel space clustering. We demonstrate more effective classification results from our algorithm by comparison with those of the classic NMF, sparse-NMF classifications and supervised classifications (KNN and SVM) for the four benchmark cancer datasets. Our algorithm can generate a family of classification algorithms in machine learning by selecting different transforms to generate subspaces and different kernel clustering algorithms to cluster data.

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Analysis of mass spectral serum profiles for biomarker selection

Cited by 80 publications

References 27 publications

A Novel Profile Biomarker Diagnosis for Mass Spectral Proteomics

A Novel Profile Biomarker Diagnosis for Mass Spectral Proteomics

Classification algorithms for phenotype prediction in genomics and proteomics

Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification

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