Download or read book Application of Random Forest Algorithm in Biomarker Discovery for Cancer Detection written by Rama Raghava Reddy Bandi and published by . This book was released on 2012 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in molecular biology and computational science have greatly influenced and promoted the field of bioinformatics. One such area is recently available high throughput platform for biomarker discovery based on Printed Glycan Arrays and new bioinformatics algorithms for feature selection and classification. This thesis focuses on implementation, application and evaluation of popular Random Forest algorithm used for biomarker discovery based on data generated with PGA. The implementation is tested on real clinical data obtained from the School of Medicine of NYU, which contain a control sample with 65 high risk subjects exposed to asbestos and a case sample with 50 subjects diagnosed with malignant mesothelioma. The results are compared with other popular approaches such as univariate feature selection based on Wilcoxon ranking, and multivariate feature selection based on Causal Networks and Markov Blanket algorithms.
Download or read book Novel Methods of Biomarker Discovery and Predictive Modeling Using Random Forest written by Xin Guan and published by . This book was released on 2017 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random forest (RF) is a popular and powerful technique nowadays. It can be used for classification, regression and unsupervised clustering. In its original form introduced by Leo Breiman, RF is used as a predictive model to generate predictions for new observations. Recent researches have proposed several methods based on RF for feature selection and for generating prediction intervals. However, they are limited in their applicability and accuracy. In this dissertation, RF is applied to build a predictive model for a complex dataset, and used as the basis for two novel methods for biomarker discovery and generating prediction interval. Firstly, a biodosimetry is developed using RF to determine absorbed radiation dose from gene expression measured from blood samples of potentially exposed individuals. To improve the prediction accuracy of the biodosimetry, day-specific models were built to deal with day interaction effect and a technique of nested modeling was proposed. The nested models can fit this complex data of large variability and non-linear relationships. Secondly, a panel of biomarkers was selected using a data-driven feature selection method as well as handpick, considering prior knowledge and other constraints. To incorporate domain knowledge, a method called Know-GRRF was developed based on guided regularized RF. This method can incorporate domain knowledge as a penalized term to regulate selection of candidate features in RF. It adds more flexibility to data-driven feature selection and can improve the interpretability of models. Know-GRRF showed significant improvement in cross-species prediction when cross-species correlation was used to guide selection of biomarkers. The method can also compete with existing methods using intrinsic data characteristics as alternative of domain knowledge in simulated datasets. Lastly, a novel non-parametric method, RFerr, was developed to generate prediction interval using RF regression. This method is widely applicable to any predictive models and was shown to have better coverage and precision than existing methods on the real-world radiation dataset, as well as benchmark and simulated datasets.
Download or read book Artificial intelligence A step forward in biomarker discovery and integration towards improved cancer diagnosis and treatment written by Mónica Hebe Vazquez-Levin and published by Frontiers Media SA. This book was released on 2023-04-26 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Biomarkers Discovery Using Network Based Anomaly Detection written by Cheng Kai Chen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying biomarkers is an important step in translating research advances in genomics into clinical practice. From a machine learning perspective, computational biomarker identification can be implemented using a broad range of feature selection methods. In this thesis, we consider an alternative approach, Network-Based Biomarker Discovery (NBBD) framework. As the name suggest, NBBD uses network representations of the input data to identify potential biomarkers (i.e., discriminative features for training machine learning classifiers). NBBD consists of two main customizable modules: Network Inference Module and Node Importance Scoring Module. The Network Inference Module creates ecological networks from given dataset. The Node Importance Scoring Module computes a score for each node based on the difference between two ecological networks. However, most of the node scoring methods used in NBBD are based on nodes' local topological properties. To date, NBBD has been successfully applied to metagenomics data.In this thesis, we extend two aspects of the earlier work on NBBD: i) we propose two novel node important scoring methods based on node anomaly scores and differences in nodes global profiles; ii) we demonstrate the applicability of NBBD for Neuroblastoma biomarker discovery from gene expression data. Our computational results show that our methods can outperform the local node importance scoring methods and are comparable to state-of-art feature selection methods, including Random Forest Feature Importance and Information Gain.
Download or read book Biomarkers in Oncology written by Heinz-Josef Lenz and published by Springer Science & Business Media. This book was released on 2012-09-18 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated book covers the entire spectrum of cancer biomarkers in development and clinical use. Predictive and prognostic markers are explored in the context of colon cancer, breast cancer, lung cancer, prostate cancer, and GIST. International experts provide insight into toxicity markers and surrogate markers. Attention is also given to biomarker assay development, validation, and strategies. A powerful tool for determining decisions on therapy, selecting drug regimens, monitoring the efficacy of treatment, and performing individualized surveillance, biomarkers represent the forefront of cancer research and treatment. As these technologies become increasingly available for clinical use, this book will be an essential resource for oncologists and translational researchers.
Download or read book Data Mining for Biomarker Discovery written by Panos M. Pardalos and published by Springer Science & Business Media. This book was released on 2012-02-11 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomarker discovery is an important area of biomedical research that may lead to significant breakthroughs in disease analysis and targeted therapy. Biomarkers are biological entities whose alterations are measurable and are characteristic of a particular biological condition. Discovering, managing, and interpreting knowledge of new biomarkers are challenging and attractive problems in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques. This volume is intended for students, and researchers in bioinformatics, proteomics, and genomics, as well engineers and applied scientists interested in the interdisciplinary application of data mining techniques.
Download or read book Proteomic Applications in Cancer Detection and Discovery written by Timothy D. Veenstra and published by John Wiley & Sons. This book was released on 2013-05-30 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Helps researchers in proteomics and oncology work together to understand, prevent, and cure cancer Proteomic data is increasingly important to understanding the origin and progression of cancer; however, most oncologic researchers who depend on proteomics for their studies do not collect the data themselves. As a result, there is a knowledge gap between scientists, who devise proteomic techniques and collect the data, and the oncologic researchers, who are expected to interpret and apply proteomic data. Bridging the gap between proteomics and oncology research, this book explains how proteomic technology can be used to address some of the most important questions in cancer research. Proteomic Applications in Cancer Detection and Discovery enables readers to understand how proteomic data is acquired and analyzed and how it is interpreted. Author Timothy Veenstra has filled the book with examples many based on his own firsthand research experience that clearly demonstrate the application of proteomic technology in oncology research, including the discovery of novel biomarkers for different types of cancers. The book begins with a brief introduction to systems biology, explaining why cancer is a systems biology disease. Next, it covers such topics as: Mass spectrometry in cancer research Application of proteomics to global phosphorylation analysis Search for biomarkers in biofluids Rise and fall of proteomic patterns for cancer diagnostics Emergence of protein arrays Role of proteomics in personalized medicine The final chapter is dedicated to the future prospects of proteomics in cancer research. By guiding readers through the latest proteomic technologies and their applications in cancer research, Proteomic Applications in Cancer Detection and Discovery enhances the ability of researchers in proteomics and researchers in oncology to collaborate in order to better understand cancer and develop strategies to prevent and treat it.
Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Download or read book Bioinformatics and Biomarker Discovery written by Francisco Azuaje and published by John Wiley & Sons. This book was released on 2011-08-24 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations
Download or read book Application of Bioinformatics in Cancers written by Chad Brenner and published by MDPI. This book was released on 2019-11-20 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.
Download or read book Intelligent Computing Theories and Application written by De-Shuang Huang and published by Springer. This book was released on 2019-09-22 with total page 790 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 11643 and LNCS 11644 constitutes - in conjunction with the volume LNAI 11645 - the refereed proceedings of the 15th International Conference on Intelligent Computing, ICIC 2019, held in Nanchang, China, in August 2019. The 217 full papers of the three proceedings volumes were carefully reviewed and selected from 609 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology.
Download or read book Bioinformatics and Computational Biology Solutions Using R and Bioconductor written by Robert Gentleman and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
Download or read book Intelligent Data Analysis in Medicine and Pharmacology written by Nada Lavrač and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent data analysis, data mining and knowledge discovery in databases have recently gained the attention of a large number of researchers and practitioners. This is witnessed by the rapidly increasing number of submissions and participants at related conferences and workshops, by the emergence of new journals in this area (e.g., Data Mining and Knowledge Discovery, Intelligent Data Analysis, etc.), and by the increasing number of new applications in this field. In our view, the awareness of these challenging research fields and emerging technologies has been much larger in industry than in medicine and pharmacology. The main purpose of this book is to present the various techniques and methods that are available for intelligent data analysis in medicine and pharmacology, and to present case studies of their application. Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96), IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of the book is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.
Download or read book Feature Selection for Data and Pattern Recognition written by Urszula Stańczyk and published by Springer. This book was released on 2016-09-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
Download or read book Machine Learning Paradigms written by George A. Tsihrintzis and published by Springer. This book was released on 2019-07-06 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
Download or read book Ensemble Machine Learning written by Cha Zhang and published by Springer Science & Business Media. This book was released on 2012-02-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
