Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

Yu, Jiangsheng; Fiedler, Roman; Chen, X. W.; Toffolo, Gianna; Cobelli, Claudio; Trajanoski, Zlatko

doi:10.1093/bioinformatics/bti370

Cited by 125 publications

(89 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Low frequency components are represented by the approximation coefficients, while the high frequency components are represented by the detail coefficients. Both the approximation and detail coefficients are used before for MS data [6], [7]. In one of these studies, it is claimed that the detailed coefficients are not sufficient for MS data classification, while in the other one, detailed coefficients are used and it is shown that the detail coefficients performed very well.…”

Section: Dimension Reduction Stagesmentioning

confidence: 99%

“…k(x i ,x j ) = ( <x i ,x j >+1) p (6) where, p=2 denotes the degree of the polynomial. A brief description of the KPLS algorithm is given in Table I.…”

Section: Dimension Reduction Stagesmentioning

confidence: 99%

“…Therefore, after a filtering process generally the obtained data will have highly correlated and thus redundant features, which will worsen the classification performance. Even though the filtering methods have the above mentioned disadvantage, they are still preferred as an initial dimension reduction step in many studies [3]- [6]. In wrapper methods, dimension reduction process is integrated into the classification stage.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, wavelet analysis and statistical moments are used for this purpose [6]- [8]. In the former one, the discrete wavelet transform (DWT) is applied to the MS data and approximation coefficients are obtained.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prostate Cancer Classification from Mass Spectrometry Data by Using Wavelet Analysis and Kernel Partial Least Squares Algorithm

Taşkın¹,

Doğan²,

Ölmez³

2013

IJBBB

View full text Add to dashboard Cite

Abstract-In this study, a three stage dimension reduction strategy is proposed for early detection of prostate cancer by using mass spectrometry data. In the initial stage, a filtering method is used. While in the second stage, two different methods namely, the wavelet analysis and statistical moments are used for comparison. The last stage includes a feature transformation method which is called kernel partial least squares algorithm. After dimension reduction stages, prostate mass spectrometry data are classified with k-nearest neighbor, support vector machines and linear discriminant analysis. The classification process is handled in two phases. In the first phase, the prostate mass spectrometry data are classified as the normal and cancerous samples with an accuracy of 95.8%. While in the second phase, the cancerous samples are classified as benign and malign samples with an accuracy of 87.2%. For each cases it is shown that, the combination of the wavelet analysis and kernel partial least squares methods is sufficient for prostate cancer identification.

show abstract

Section: Dimension Reduction Stagesmentioning

confidence: 99%

“…k(x i ,x j ) = ( <x i ,x j >+1) p (6) where, p=2 denotes the degree of the polynomial. A brief description of the KPLS algorithm is given in Table I.…”

Section: Dimension Reduction Stagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prostate Cancer Classification from Mass Spectrometry Data by Using Wavelet Analysis and Kernel Partial Least Squares Algorithm

Taşkın¹,

Doğan²,

Ölmez³

2013

IJBBB

View full text Add to dashboard Cite

show abstract

“…This is beyond the scope of our work. We will use peakbins in the data to identify biomarkers after a binning step as in [27,82].…”

Section: Introductionmentioning

confidence: 99%

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

García-Torres

Armañanzas

Bielza

et al. 2013

Information Sciences

View full text Add to dashboard Cite

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

show abstract

Guest Commentary (1) on Chapter 10: Towards Building Knowledge‐Based Assistants for Intelligent Data Analysis in Biomarker Discovery

Bellazzi

Azuaje²

2010

Bioinformatics and Biomarker Discovery

View full text Add to dashboard Cite

Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

Abstract: The software is available for academic and non-commercial institutions.

Cited by 125 publications

References 22 publications

Prostate Cancer Classification from Mass Spectrometry Data by Using Wavelet Analysis and Kernel Partial Least Squares Algorithm

Prostate Cancer Classification from Mass Spectrometry Data by Using Wavelet Analysis and Kernel Partial Least Squares Algorithm

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Guest Commentary (1) on Chapter 10: Towards Building Knowledge‐Based Assistants for Intelligent Data Analysis in Biomarker Discovery

Contact Info

Product

Resources

About