Download or read book Machine Learning and Statistical Approaches to the Quality Classification of Tandem Mass Spectrometry written by Lijuan Mo and published by . This book was released on 2006 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning Methods for the Analysis of Liquid Chromatography mass Spectrometry Datasets in Metabolomics written by Francesc Fernández Albert and published by . This book was released on 2014 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liquid Chromatography-Mass Spectrometry (LC/MS) instruments are widely used in Metabolomics. To analyse their output, it is necessary to use computational tools and algorithms to extract meaningful biological information. The main goal of this thesis is to provide with new computational methods and tools to process and analyse LC/MS datasets in a metabolomic context. A total of 4 tools and methods were developed in the context of this thesis. First, it was developed a new method to correct possible non-linear drift effects in the retention time of the LC/MS data in Metabolomics, and it was coded as an R package called HCor. This method takes advantage of the retention time drift correlation found in typical LC/MS data, in which there are chromatographic regions in which their retention time drift is consistently different than other regions. Our method makes the hypothesis that this correlation structure is monotonous in the retention time and fits a non-linear model to remove the unwanted drift from the dataset. This method was found to perform especially well on datasets suffering from large drift effects when compared to other state-of-the art algorithms. Second, it was implemented and developed a new method to solve known issues of peak intensity drifts in metabolomics datasets. This method is based on a two-step approach in which are corrected possible intensity drift effects by modelling the drift and then the data is normalised using the median of the resulting dataset. The drift was modelled using a Common Principal Components Analysis decomposition on the Quality Control classes and taking one, two or three Common Principal Components to model the drift space. This method was compared to four other drift correction and normalisation methods. The two-step method was shown to perform a better intensity drift removal than all the other methods. All the tested methods including the two-step method were coded as an R package called intCor and it is publicly available. Third, a new processing step in the LC/MS data analysis workflow was proposed. In general, when LC/MS instruments are used in a metabolomic context, a metabolite may give a set of peaks as an output. However, the general approach is to consider each peak as a variable in the machine learning algorithms and statistical tests despite the important correlation structure found between those peaks coming from the same source metabolite. It was developed an strategy called peak aggregation techniques, that allow to extract a measure for each metabolite considering the intensity values of the peaks coming from this metabolite across the samples in study. If the peak aggregation techniques are applied on each metabolite, the result is a transformed dataset in which the variables are no longer the peaks but the metabolites. 4 different peak aggregation techniques were defined and, running a repeated random sub-sampling cross-validation stage, it was shown that the predictive power of the data was improved when the peak aggregation techniques were used regardless of the technique used. Fourth, a computational tool to perform end-to-end analysis called MAIT was developed and coded under the R environment. The MAIT package is highly modular and programmable which ease replacing existing modules for user-created modules and allow the users to perform their personalised LC/MS data analysis workflows. By default, MAIT takes the raw output files from an LC/MS instrument as an input and, by applying a set of functions, gives a metabolite identification table as a result. It also gives a set of figures and tables to allow for a detailed analysis of the metabolomic data. MAIT even accepts external peak data as an input. Therefore, the user can insert peak table obtained by any other available tool and MAIT can still perform all its other capabilities on this dataset like a classification or mining the Human Metabolome Dataset which is included in the package.
Download or read book Advances in Mass Data Analysis of Images and Signals in Medicine Biotechnology Chemistry and Food Industry written by Petra Perner and published by Springer Science & Business Media. This book was released on 2008-07-04 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automatic analysis of signals and images together with the characterization and elaboration of their representation features is still a challenging activity in many relevant scientific and hi-tech fields such as medicine, biotechnology, and chemistry. Multidimensional and multisource signal processing can generate a number of information patterns which can be useful to increase the knowledge of several domains for solving complex problems. Furthermore, advanced signal and image manipulation allows relating specific application problems into pattern recognition problems, often implying also the development of KDD and other computational intelligence procedures. Nevertheless, the amount of data produced by sensors and equipments used in biomedicine, biotechnology and chemistry is usually quite huge and structured, thus strongly pushing the need of investigating advanced models and efficient computational algorithms for automating mass analysis procedures. Accordingly, signal and image understanding approaches able to generate automatically expected outputs become more and more essential, including novel conceptual approaches and system architectures. The purpose of this third edition of the International Conference on Mass Data Analysis of Signals and Images in Medicine, Biotechnology, Chemistry and Food Industry (MDA 2008; www.mda-signals.de) was to present the broad and growing scientific evidence linking mass data analysis with challenging problems in medicine, biotechnology and chemistry. Scientific and engineering experts convened at the workshop to present the current understanding of image and signal processing and interpretation methods useful for facing various medical and biological problems and exploring the applicability and effectiveness of advanced techniques as solutions.
Download or read book High Performance Algorithms for Mass Spectrometry Based Omics written by Fahad Saeed and published by Springer Nature. This book was released on 2022-09-02 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.
Download or read book Statistical Inference and Classification for Mass Spectrometry MS Data written by Mourad Atlas and published by . This book was released on 2009 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mass spectrometry has emerged as a core technology for high throughput proteomics profiling in biomedical research. However, the complexity of the data poses new statistical challenges for the analysis. Statistical methods and software developments for analyzing proteomics data are likely to continue as a major area of research in the coming years. In this dissertation, we develop a novel statistical method for analyzing MS data. We propose to use the chemical knowledge regarding isotopic distribution of the peptide molecules along with quantitative modeling to detect chemically valuable peaks from each spectrum. More specifically, a mixture of location-shifted Poisson distribution is fitted to the deamidated isotopic distribution of a peptide molecule in low to moderate molecular weight of the mass spectrum. Maximum likelihood estimation by the expectation-maximization (EM) technique is used to estimate the parameters of the distribution. We then identify the monoisotopic peaks of the spectrum through formal statistical hypotheses testing procedures. Unlike low to moderate range MS data a Poisson distribution is not suitable for high mass ranges of the spectrum data due to symmetric nature of the isotopic distribution. Also, due to preprocessing and pronounced effect of the additional sources of variability, a Poisson approximation to the binomial model to the isotopic distribution may not hold. Therefore, a mixture of location-shifted Normal model is fitted to model each of the deamidated (possibly) isotopic distribution of a mass spectrum. A nonlinear optimization method to maximize the observed data likelihood is applied instead of EM algorithm to estimate the parameters of the distribution. Similar statistical testing procedures are applied for the peak detection method. A study of the effectiveness of our features selection method compared to some other relatively new feature selection methods in classifying case and control samples is explored. Superiority of our method is established in terms of the overall classification accuracy, sensitivity, specificity and area under the receptor operative curve (ROC) curve.
Download or read book Tandem Mass Spectrometry written by Ana Varela Coelho and published by BoD – Books on Demand. This book was released on 2013-05-29 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tandem Mass Spectrometry - Molecular Characterization presents a comprehensive coverage of theory, instrumentation and description of experimental strategies and MS/MS data interpretation for the structural characterization of relevant molecular compounds. The areas covered include the analysis of drugs, metabolites, carbohydrates and protein post-translational modifications. The book series in Tandem Mass Spectrometry serves multiple groups of audiences; professional (academic and industry), graduate students and general readers interested in the use of modern mass spectrometry in solving critical questions of chemical and biological sciences.
Download or read book Microbiological Identification using MALDI TOF and Tandem Mass Spectrometry written by Haroun N. Shah and published by John Wiley & Sons. This book was released on 2023-03-29 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbiological Identification using MALDI-TOF and Tandem Mass Spectrometry Detailed resource presenting the capabilities of MALDI mass spectrometry (MS) to industrially and environmentally significant areas in the biosciences Microbiological Identification using MALDI-TOF and Tandem Mass Spectrometry fulfills a need to bring the key analytical technique of MALDI mass spectrometric analysis into routine practice by specialists and non-specialists, and technicians. It informs and educates established researchers on the development of techniques as applied to industrially significant areas within the biosciences. Throughout the text, the reader is presented with recognized and emerging techniques of this powerful and continually advancing field of analytical science to key areas of importance. While many scientific papers are reporting new applications of MS-based analysis in specific foci, this book is unique in that it draws together an incredibly diverse range of applications that are pushing the boundaries of MS across the broad field of biosciences. Contributed to by recognized experts in the field of MALDI MS who have been key players in promoting the advancement and dissemination of authoritative information in this field, Microbiological Identification using MALDI-TOF and Tandem Mass Spectrometry covers sample topics such as: Oil microbiology, marine and freshwater ecosystems, agricultural and food microbiology, and industrial waste microbiology Bioremediation and landfill sites microbiology, microbiology of inhospitable sites (e.g. Arctic and Antarctic, and alkaline and acidic sites, and hot temperatures) Veterinary, poultry and animals, viral applications of MS, and antibiotic resistance using tandem MS methods Recent developments which are set to transform the use of MS from its success in clinical microbiology to a wide range of commercial and environmental uses Bridging the gap between measurement and key applications, this text is an ideal resource for industrial and environmental analytical scientists, including technologists in the food industry, pharmaceuticals, and agriculture, as well as biomedical scientists, researchers, clinicians and academics and scientists in bio-resource centers.
Download or read book Statistical Approaches to Analyze Mass Spectrometry Data written by Soyoung Ryu and published by . This book was released on 2011 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Analysis of Proteomics Metabolomics and Lipidomics Data Using Mass Spectrometry written by Susmita Datta and published by Springer. This book was released on 2016-12-15 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.
Download or read book Pre processing of Tandem Mass Spectra Using Machine Learning Methods written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Classification and Data Science in the Digital Age written by Paula Brito and published by Springer. This book was released on 2023-08-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.
Download or read book Ensemble Classification Methods with Applications in R written by Esteban Alfaro and published by John Wiley & Sons. This book was released on 2018-11-05 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classification has become an extension of the individual classifiers. The text puts the emphasis on two areas of machine learning: classification trees and ensemble learning. The authors explore ensemble classification methods’ basic characteristics and explain the types of problems that can emerge in its application. Written by a team of noted experts in the field, the text is divided into two main sections. The first section outlines the theoretical underpinnings of the topic and the second section is designed to include examples of practical applications. The book contains a wealth of illustrative cases of business failure prediction, zoology, ecology and others. This vital guide: Offers an important text that has been tested both in the classroom and at tutorials at conferences Contains authoritative information written by leading experts in the field Presents a comprehensive text that can be applied to courses in machine learning, data mining and artificial intelligence Combines in one volume two of the most intriguing topics in machine learning: ensemble learning and classification trees Written for researchers from many fields such as biostatistics, economics, environment, zoology, as well as students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine learning: classification trees and ensemble learning.
Download or read book A Machine Learning Pipeline Preprocessing and Classification of Mass Spectrometry Data written by Leon Huber and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning Approaches to Refining Post translational Modification Predictions and Protein Identifications from Tandem Mass Spectrometry written by Clement Chung and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pattern Classification Using Ensemble Methods written by Lior Rokach and published by World Scientific. This book was released on 2009-11-30 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Download or read book Detection and Analysis of Microorganisms by Mass Spectrometry written by Jia Yi and published by Royal Society of Chemistry. This book was released on 2023-10-06 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Functional Genomics and Proteomics in the Clinical Neurosciences written by Scott E. Hemby and published by Elsevier. This book was released on 2006-10-09 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this work is to familiarize neuroscientists with the available tools for proteome research and their relative abilities and limitations. To know the identities of the thousands of different proteins in a cell, and the modifications to these proteins, along with how the amounts of both of these change in different conditions would revolutionize biology and medicine. While important strides are being made towards achieving the goal of global mRNA analysis, mRNA is not the functional endpoint of gene expression and mRNA expression may not directly equate with protein expression. There are many potential applications for proteomics in neuroscience: determination of the neuro-proteome, comparative protein expression profiling, post-translational protein modification profiling and mapping protein-protein interactions, to name but a few. Functional Genomics and Proteomics in Clinical Neuroscience will comment on all of these applications, but with an emphasis on protein expression profiling. This book combines the basic methodology of genomics and proteomics with the current applications of such technologies in understanding psychiatric illnesses. * Introduction of basic methodologies in genomics and proteomics and their integration in psychiatry* Development of the text in sections related to methods, application and future directions of these rapidly advancing technologies* Use of actual data to illustrate many principles of functional genomics and proteomics. * Introduction to bioinformatics and database management techniques
