Detection of ovarian cancer using chemometric analysis of proteomic profiles

Whelehan, O. P.; Earll, Mark; Johansson, Erik; Toft, Marianne; Eriksson, Lennart

doi:10.1016/j.chemolab.2006.03.008

Cited by 43 publications

(38 citation statements)

References 19 publications

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“…A correct prediction of different tumor types has noticeable value in providing better treatment and toxicity minimization on the patients. The early diagnosis of cancer can significantly reduce mortality rates among the patients [1]. On the other hand cancer classification and detection have always been morphological and clinical based while using conventional methods have own several restrictions in their diagnostic ability [1].…”

Section: Introductionmentioning

confidence: 99%

“…The early diagnosis of cancer can significantly reduce mortality rates among the patients [1]. On the other hand cancer classification and detection have always been morphological and clinical based while using conventional methods have own several restrictions in their diagnostic ability [1]. In order to increase a better insight into the problem of cancer classification, systematic approaches based on global gene expression analysis have been proposed [2].…”

Section: Introductionmentioning

confidence: 99%

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Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

Karimi

Farrokhnia

2014

Chemometrics and Intelligent Laboratory Systems

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Using gene expression data in cancer classification plays an important role for solving the fundamental problems relating to cancer diagnosis. Because of high throughput of gene expression data for healthy and patient samples, a variable selection method can be applied to reduce complexity of the model and improve the classification performance. Since variable selection procedures pose a risk of over-fitting, when a large number of variables with respect to sample are used, we have proposed a method for coupling data dimension reduction and variable selection in the present study. This approach uses the concept of variable clustering for the original data set. Significant components of local principal component analysis models have just been retained from all clusters. Then, the variable selection algorithm is performed on these locally derived principal component variables. The proposed algorithm has been evaluated on two gene expression data sets; namely, acute Leukemia and small round blue-cell tumor (SRBCT). Our results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire microarray gene expression profile.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

Karimi

Farrokhnia

2014

Chemometrics and Intelligent Laboratory Systems

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“…Supervised techniques, such as partial least squares-discriminant analysis (PLS-DA), have also been successfully used to discriminate tumor from normal tissue, [33][34][35] such as kidney and bladder cancers using the information provided by DESI-MS on the tissue lipids profiles. 4,8 The support vector machine (SVM) approach has also been applied to MS data, using individual mass spectra for each sample to achieve classification.…”

Section: Introductionmentioning

confidence: 99%

Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections

Pirro

Eberlin

Oliveri

et al. 2012

Analyst

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“…However, such a relationship is quite complicated and is difficult to explain very satisfactorily through the investigation of one or a few trace elements due to interactions among various trace elements [14]. Therefore, it is often the case that a classification/regression tool is used to model the quantitative/qualitative relationship between trace elements and diseases [15][16][17]. Seeking an appropriate tool for such tasks has been a main mission in chemometrics.…”

Section: Introductionmentioning

confidence: 99%

Application of Boosting Classification and Regression to Modeling the Relationships Between Trace Elements and Diseases

Tan

Chen

Zhu

2009

Biol Trace Elem Res

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The study on the relationship between trace elements and diseases often need to build a classification/regression model. Furthermore, the accuracy of such a model is of particular importance and directly decides its applicability. The goal of this study is to explore the feasibility of applying boosting, i.e., a new strategy from machine learning, to model the relationship between trace elements and diseases. Two examples are employed to illustrate the technique in the applications of classification and regression, respectively. The first example involves the diagnosis of anorexia according to the concentrations of six elements (i.e. classification task). Decision stump and support vector machine are used as the weak/base algorithm and reference algorithm, respectively. The second example involves the prediction of breast cancer mortality based on the intake of trace elements (i.e. a regression task). In this regard, partial least squares is not only used as the weak/base algorithm, but also the reference algorithm. The results from both examples confirm the potential of boosting in modeling the relationship between trace elements and diseases.

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Detection of ovarian cancer using chemometric analysis of proteomic profiles

Cited by 43 publications

References 19 publications

Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections

Application of Boosting Classification and Regression to Modeling the Relationships Between Trace Elements and Diseases

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