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Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
The thesis focused on data analysis of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker discovery and cancer classification. It investigated quantitative measures of reproducibility and found that SELDI protein profiles are affected by sample storage and processing procedure. Two new peak alignment algorithms were proposed, one of which achieved the best performance when compared to the existing methods. The assumption of normality of SELDI protein profiles, on which the standard statistical methods are based, was examined. Normality tests and the multiple testing procedures revealed that SELDI protein profiles do not follow normal distributions, implying that it may be reliable to use non-parametric methods for detecting disease-associated proteins. A new normalisation algorithm was proposed, and was shown to give a better improvement of normality compared with the existing methods. An integrated algorithm to discover proteomic biomarkers for cancer diagnosis was proposed and applied to two published SELDI data sets. The results demonstrated that the receiver operating characteristic (ROC) curve method may be more reliable to determine the discriminatory powers of the identified biomarkers compared to Wilcoxon test. The methods for proteomic biomarker discovery presented here may be generalisable and applicable to other mass spectrometry and genomics approaches.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
The thesis focused on data analysis of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker discovery and cancer classification. It investigated quantitative measures of reproducibility and found that SELDI protein profiles are affected by sample storage and processing procedure. Two new peak alignment algorithms were proposed, one of which achieved the best performance when compared to the existing methods. The assumption of normality of SELDI protein profiles, on which the standard statistical methods are based, was examined. Normality tests and the multiple testing procedures revealed that SELDI protein profiles do not follow normal distributions, implying that it may be reliable to use non-parametric methods for detecting disease-associated proteins. A new normalisation algorithm was proposed, and was shown to give a better improvement of normality compared with the existing methods. An integrated algorithm to discover proteomic biomarkers for cancer diagnosis was proposed and applied to two published SELDI data sets. The results demonstrated that the receiver operating characteristic (ROC) curve method may be more reliable to determine the discriminatory powers of the identified biomarkers compared to Wilcoxon test. The methods for proteomic biomarker discovery presented here may be generalisable and applicable to other mass spectrometry and genomics approaches.
Author: Yaping Cheng Publisher: ISBN: Category : Languages : en Pages : 244
Book Description
The thesis focused on data analysis of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker discovery and cancer classification. It investigated quantitative measures of reproducibility and found that SELDI protein profiles are affected by sample storage and processing procedure. Two new peak alignment algorithms were proposed, one of which achieved the best performance when compared to the existing methods. The assumption of normality of SELDI protein profiles, on which the standard statistical methods are based, was examined. Normality tests and the multiple testing procedures revealed that SELDI protein profiles do not follow normal distributions, implying that it may be reliable to use non-parametric methods for detecting disease-associated proteins. A new normalisation algorithm was proposed, and was shown to give a better improvement of normality compared with the existing methods. An integrated algorithm to discover proteomic biomarkers for cancer diagnosis was proposed and applied to two published SELDI data sets. The results demonstrated that the receiver operating characteristic (ROC) curve method may be more reliable to determine the discriminatory powers of the identified biomarkers compared to Wilcoxon test. The methods for proteomic biomarker discovery presented here may be generalisable and applicable to other mass spectrometry and genomics approaches.
Author: K.D. Rodland Publisher: IOS Press ISBN: 9781586034610 Category : Medical Languages : en Pages : 56
Book Description
One of the major goals of twenty-first century medicine is the identification of biomarkers for the earliest possible stages of disease involvement so that prompt clinical intervention can limit damage, reverse pathological change, and ideally effect a complete cure. New developments in the analytical field of mass spectrometry are providing clinicians and translational scientists with even more powerful tools for the discovery of novel biomarkers. A key requirement for the successful application of mass spectrometric approaches to biomarker discovery is a clear reference standard for the 'normal' human proteome, and a better understanding of the sources and extent of 'normal' variability in human plasma proteins. This special issue of Disease Markers includes examples of biomarker discovery efforts directed at breast and prostate cancers, diabetes mellitus, and heart disease.
Author: Charlotte H. Clarke Publisher: Humana Press ISBN: 9781493961313 Category : Science Languages : en Pages : 252
Book Description
Coupling features of chromatography and mass spectrometry, SELDI is a popular method of biomarker discovery. This volume reviews its current applications, emphasizing study and experimental design, data analysis and interpretation, and assay development.
Author: John Roboz Publisher: CRC Press ISBN: 1420042696 Category : Medical Languages : en Pages : 577
Book Description
Cancer research is becoming multidisciplinary. The complex structural and therapeutic problems require synergistic approaches employing an assortment of biochemical manipulations, chromatographic or electrophoretic separations, sequencing strategies, and more and more mass spectrometry. Mass Spectrometry in Cancer Research provides a broad
Author: T.D. Veenstra Publisher: IOS Press ISBN: 9781586034344 Category : Diagnosis Languages : en Pages : 88
Book Description
For many diseases, such as heart disease and cancer, early detection plays a pivotal role in the survival rate of the patient. When detected early, many such lethal diseases can be effectively treated with existing remedies. The difficulty remains, however, how to effectively detect such conditions at the earliest possible stage with a high enough positive predictive value so that they can be treated effectively without overwhelming the medical system with false positive diagnoses. What is required is the identification of more effective or additional biomarkers, as well as other types of technologies, that can aid in the diagnosis of early stage diseases. The challenge is how to identify more effective biomarkers or technologies that can provide an earlier indication of a disease with a higher positive predictive value than presently utilized methods. Proteomics, along with genomics and transcriptomics, has benefited greatly from the development of high-throughput methods to study thousands of proteins almost simultaneously.Based on the rate of interesting leads already being discovered using proteomics, it is likely that not only will biomarkers with better sensitivity and specificity be identified but individuals will be treated using customized therapies based on their specific protein profile. Since many of the proteomic technologies and data management tools are still in their infancy, the future of proteomics in disease diagnostics looks extremely promising.
Author: Ao Kong Publisher: ISBN: Category : Data mining Languages : en Pages :
Book Description
Early detection of cancer is crucial for successful intervention strategies. Mass spectrometry-based high throughput proteomics is recognized as a major breakthrough in cancer detection. Many machine learning methods have been used to construct classifiers based on mass spectrometry data for discriminating between cancer stages, yet, the classifiers so constructed generally lack biological interpretability. To better assist clinical uses, a key step is to discover ”biomarker signature profiles”, i.e. combinations of a small number of protein biomarkers strongly discriminating between cancer states. This dissertation introduces two innovative algorithms to automatically search for a signature and to construct a high-performance signature-based classifier for cancer discrimination tasks based on mass spectrometry data, such as data acquired by MALDI or SELDI techniques. Our first algorithm assumes that homogeneous groups of mass spectra can be modeled by (unknown) Gibbs distributions to generate an optimal signature and an associated signature-based classifier by robust log-likelihood analysis; our second algorithm uses a stochastic optimization algorithm to search for two lists of biomarkers, and then constructs a signature-based classifier. To support these two algorithms theoretically, this dissertation also studies the empirical probability distributions of mass spectrometry data and implements the actual fitting of Markov random fields to these high-dimensional distributions. We have validated our two signature discovery algorithms on several mass spectrometry datasets related to ovarian cancer and to colorectal cancer patients groups. For these cancer discrimination tasks, our algorithms have yielded better classification performances than existing machine learning algorithms and in addition,have generated more interpretable explicit signatures.
Author: 
Author: 
Author: Yaping Cheng
Author: Yaping Cheng
Author: K.D. Rodland
Author: Charlotte H. Clarke
Author: John Roboz
Author: Jingjing Ye
Author: T.D. Veenstra
Author: Ao Kong
Author: Eleftherios P. Diamandis