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Multivariate Biomarker Discovery

Multivariate Biomarker Discovery
Author: Darius M. Dziuda
Publisher: Cambridge University Press
Total Pages: 295
Release: 2024-04-30
Genre: Science
ISBN: 100900770X
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Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas.

Biomarker Discovery in the Developing World: Dissecting the Pipeline for Meeting the Challenges

Biomarker Discovery in the Developing World: Dissecting the Pipeline for Meeting the Challenges
Author: Sanjeeva Srivastava
Publisher: Springer
Total Pages: 120
Release: 2016-09-30
Genre: Medical
ISBN: 8132228375
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This book is oriented towards post-graduates and researchers with interest in proteomics and its applications in clinical biomarker discovery pipeline. Biomarker discovery has long been the research focus of many life scientists globally. However, the pipeline starting from discovery to validation to regulation as a diagnostic or therapeutic molecule follows a complex trajectory. This book aims to provide an in-depth synopsis on each of these developmental phases attendant to biomarker “life cycle” with emphasis on the emerging and significant role of proteomics. The book begins with a perspective on the role of biorepositories and need for biobanking practices in the developing world. The next chapter focuses on disease heterogeneity in context to geographical bias towards susceptibility to the disease and the role of multi-omics techniques to devise disruptive innovations towards biomarker discovery. Chapter 3 focuses on various omics-based platforms that are currently being used for biomarker discovery, their principles and workflow. Mass spectrometry is emerging as a powerful technology for discovery based studies and targeted validation. Chapter 4 aims at providing a glimpse of the basic workflow and considerations in mass spectrometry based studies. Rapid and aptly targeted research funding has often been deemed as one of the decisive factors enabling excellent science and path breaking innovations. With the need for sophistication required in multi-omics research, Chapter 5 focuses on innovative funding strategies such as crowdfunding and Angel philanthropy. Chapter 6 provides the latest advances in education innovation, the premise and reality of bioeconomy especially in a specific context of the developing world, not to mention the new concept of “social innovation” to link biomarkers with socially responsible and sustainable applications. Chapter 7, in ways similar to biomarkers, discusses the biosimilars as a field that has received much focus and prominence recently due to their immense potential in clinical and pharmaceutical innovation literatures. The broader goal post-biomarker discovery is to translate their use in clinics. However, the road from bench-to-bed side is arduous and complex that is subject to oversight from various national and international regulatory bodies. Chapter 8 underscores these regulatory science considerations and provides a concise overview on intellectual property rights in biomarker discovery. Thus, this book contributed by eminent biomarker scientists, clinicians, translational researchers and social scientists holistically covers the various facets of the biomarker discovery journey from “cell to society” in developing world. The lessons learned and highlighted here are of interest to the life sciences community in a global and interdependent world.

Data Mining for Genomics and Proteomics

Data Mining for Genomics and Proteomics
Author: Darius M. Dziuda
Publisher: John Wiley & Sons
Total Pages: 348
Release: 2010-07-16
Genre: Computers
ISBN: 0470593407
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Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

Cancer Biomarkers

Cancer Biomarkers
Author: Mahmoud H. Hamdan
Publisher: Wiley-Interscience
Total Pages: 0
Release: 2007-02-09
Genre: Science
ISBN: 9780471745167
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Tools, techniques, and progress in cancer biomarkers discovery The completion of a number of gene sequencing projects, recent advances in genomic and proteomic technologies, and the availability of powerful bioinformatics tools have led to promising new avenues and approaches in the search for cancer biomarkers. This book provides a comprehensive overview of current methodologies and technologies. It discusses biomarker discovery as a whole, rather than focusing on one specific marker or cancer. With information on both existing and potential biomarkers, Cancer Biomarkers: Analytical Techniques for Discovery: * Provides insights into the current technological platforms for biomarker discovery, including mass spectrometry combined with multidimensional chromatography, DIGE, and various chip technologies * Includes a detailed discussion of protein networks and protein phosphorylation in cancer * Details the use of imaging mass spectrometry, laser capture microdissection, serial analysis of gene expression, enzyme-linked immunosorbent assays, protein microarrays, antibody-based microarrays, and bioinformatics * Covers the emerging role of surface-enhanced laser desorption ionization (SELDI) and various tagging and labeling strategies * Discusses related regulatory and ethical issues With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this is a core reference for biomarker researchers, scientists working in proteomics and bioinformatics, pharmaceutical scientists, oncologists, biochemists, biologists, and chemists.

Biomarker Analysis in Clinical Trials with R

Biomarker Analysis in Clinical Trials with R
Author: Nusrat Rabbee
Publisher: CRC Press
Total Pages: 168
Release: 2020-03-11
Genre: Mathematics
ISBN: 0429766793
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The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry, much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating, understanding and synthesizing biomarker data. -From the Foreword, Jared Christensen, Vice President, Biostatistics Early Clinical Development, Pfizer, Inc. Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R, the book helps students, researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features: Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code, data and models are given for each part: including regression models for survival and longitudinal data, as well as statistical learning models, such as graphical models and penalized regression models.

Multivariable Analysis

Multivariable Analysis
Author: Mitchell H. Katz
Publisher: Cambridge University Press
Total Pages: 228
Release: 2006-02-09
Genre: Medical
ISBN: 9780521549851
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How to perform and interpret multivariable analysis, using plain language rather than complex derivations.

Development and Evaluation of Statistical Approaches in Proteomic Biomarker Discovery

Development and Evaluation of Statistical Approaches in Proteomic Biomarker Discovery
Author: Amit Patel
Publisher:
Total Pages:
Release: 2011
Genre:
ISBN:
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A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. The aim of this project was to deal with the identification of potential biomarker candidates from experimental data comparing samples displaying divergent physiological traits. Chapter 1 introduces the topic and the aims of the project. The primary aim was to identify the ideal statistical analysis methods and data pre- and post-treatment options to use for potential biomarker identification from proteomic datasets. The product of this work was a statistical analysis pipeline for identifying potential biomarker candidates from proteomic experimental data. Proteomic data often suffers from missing values, so methods to deal with these were also evaluated in this project. Chapter 2 outlines the data sets that were used as well as presenting an overview of the "Biomarker Hunter" pipeline software solution created in this project. Chapter 3 evaluates the appropriate univariate statistical methods to use for biomarker identification and the results of biomarker identification using these techniques. Chapter 4 evaluates options for data pre- and post-processing. Chapter 5 suggests the use of missing value imputation as well as offering a novel clustering algorithm to deal with missing values. The software pipeline also offers multivariate statistical methods, which are evaluated in Chapter 6. Chapter 7 provides some business context for both biomarker discovery and the statistical analysis software available for the purpose of proteomic biomarker discovery. As well as providing a software pipeline for the identification of biomarkers, the project aimed to identify a suggested strategy for statistical analysis of proteomic experimental data. Strong conclusions regarding the ideal statistical approach could only be made if the list of actual, validated biomarkers were available. Unfortunately this information was not available, but in the absence of this a strategy was suggested based on the available information from both the available literature and the author's interpretation of the results from this study. In terms of data pre-processing, this strategy involved not averaging technical replicates, and using total abundance normalisation to reduce technical variation. A novel clustering algorithm was suggested to reduce the presence of missing values prior to existing methods of missing value imputation. Following statistical analysis multiple testing correction methods should be implemented to reduce the number of false positives.

Mass Spectrometry-Based Metabolomics in Clinical and Herbal Medicines

Mass Spectrometry-Based Metabolomics in Clinical and Herbal Medicines
Author: Aihua Zhang
Publisher: John Wiley & Sons
Total Pages: 292
Release: 2021-08-20
Genre: Science
ISBN: 3527835741
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Highlights the importance and benefit of mass spectrometry-based metabolomics for identifying biomarkers that accurately screen for potential biomarkers of diseases Mass spectrometry-based metabolomics offer new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has been applied to discovering and identifying the perturbed pathways. Currently, mass spectrometry-based metabolomics has become an important tool in clinical research and the diagnosis of human disease. Mass Spectrometry-Based Metabolomics in Clinical and Herbal Medicines comprehensively presents the current state, challenges, and applications of high-throughput mass spectrometry-based metabolomics such as metabolites analysis, biomarker discovery, technical challenges, discovery of natural product, mechanism interpretation of action, discovery of active ingredients, clinical application and precision medicine, and enhancing their biomedical value in a real world of biomedicine, shedding light on the potential for spectrometry-based metabolomics. It highlights the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. Each chapter has been laid out with introduction, method, up-to-date literature, identification of biomarker, and applications Covers the current state, challenges, and applications of high-throughput mass spectrometry-based metabolomics in the discovery of biomarker, active ingredients, natural product, etc. Constitutes a unique and indispensable practical guide for any phytochemistry or related laboratory, and provides hands-on description of new techniques Provides a guide for new practitioners of pharmacologists, pharmacological scholars, drug developers, botanist, researchers of traditional medicines. Mass Spectrometry-Based Metabolomics in Clinical and Herbal Medicines provides a landmark of mass spectrometry-based metabolomics research and a beneficial guideline to graduate students and researchers in academia, industry, and technology transfer organizations in all biomedical science fields.