Analyzing High Dimensional Microarray Data Using Variational Som Microform

Download Analyzing High Dimensional Microarray Data Using Variational Som Microform full books in PDF, epub, and Kindle. Read online Analyzing High Dimensional Microarray Data Using Variational Som Microform ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

New Perspectives in Partial Least Squares and Related Methods

Author : Herve Abdi
Publisher : Springer Science & Business Media
ISBN 13 : 1461482836
Total Pages : 351 pages
Book Rating : 4.4/5 (614 download)

DOWNLOAD NOW!

Book Synopsis New Perspectives in Partial Least Squares and Related Methods by : Herve Abdi

Download or read book New Perspectives in Partial Least Squares and Related Methods written by Herve Abdi and published by Springer Science & Business Media. This book was released on 2013-10-17 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed research from presentations during the 2012 partial least squares methods meeting (PLS 2012). This was the 7th meeting in the series of PLS conferences and the first to take place in the USA. PLS is an abbreviation for Partial Least Squares and is also sometimes expanded as projection to latent structures. This is an approach for modeling relations between data matrices of different types of variables measured on the same set of objects. The twenty-two papers in this volume, which include three invited contributions from our keynote speakers, provide a comprehensive overview of the current state of the most advanced research related to PLS and related methods. Prominent scientists from around the world took part in PLS 2012 and their contributions covered the multiple dimensions of the partial least squares-based methods. These exciting theoretical developments ranged from partial least squares regression and correlation, component based path modeling to regularized regression and subspace visualization. In following the tradition of the six previous PLS meetings, these contributions also included a large variety of PLS approaches such as PLS metamodels, variable selection, sparse PLS regression, distance based PLS, significance vs. reliability, and non-linear PLS. Finally, these contributions applied PLS methods to data originating from the traditional econometric/economic data to genomics data, brain images, information systems, epidemiology, and chemical spectroscopy. Such a broad and comprehensive volume will also encourage new uses of PLS models in work by researchers and students in many fields.

Machine Learning

Author : Maria Johnsen
Publisher : Maria Johnsen
ISBN 13 :
Total Pages : 550 pages
Book Rating : 4.3/5 (323 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning by : Maria Johnsen

Download or read book Machine Learning written by Maria Johnsen and published by Maria Johnsen. This book was released on 2024-07-06 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has revolutionized industries, from healthcare to entertainment, by enhancing how we understand and interact with data. Despite its prevalence, mastering this field requires both theoretical knowledge and practical skills. This book bridges that gap, starting with foundational concepts and essential mathematics, then advancing through a wide range of algorithms and techniques. It covers supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning, with clear explanations and practical examples. Real-world applications are highlighted through scenarios and case studies, demonstrating how to solve specific problems with machine learning. You'll find hands-on guides to popular tools and libraries like Python, Scikit-Learn, TensorFlow, Keras, and PyTorch, enabling you to build, evaluate, and deploy models effectively. The book explores cutting-edge topics like quantum machine learning and explainable AI, keeping you updated on the latest trends. Detailed case studies and capstone projects provide practical experience, guiding you through the entire machine learning process. This book, a labor of love born from extensive research and passion, aims to make machine learning accessible and engaging. Machine learning is about curiosity, creativity, and the pursuit of knowledge. Explore, experiment, and enjoy the journey. Thank you for choosing this book. I am excited to be part of your machine learning adventure and look forward to the incredible things you will achieve.

Mathematical Analysis of Evolution, Information, and Complexity

Author : Wolfgang Arendt
Publisher : John Wiley & Sons
ISBN 13 : 3527628037
Total Pages : 502 pages
Book Rating : 4.5/5 (276 download)

DOWNLOAD NOW!

Book Synopsis Mathematical Analysis of Evolution, Information, and Complexity by : Wolfgang Arendt

Download or read book Mathematical Analysis of Evolution, Information, and Complexity written by Wolfgang Arendt and published by John Wiley & Sons. This book was released on 2009-07-10 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Analysis of Evolution, Information, and Complexity deals with the analysis of evolution, information and complexity. The time evolution of systems or processes is a central question in science, this text covers a broad range of problems including diffusion processes, neuronal networks, quantum theory and cosmology. Bringing together a wide collection of research in mathematics, information theory, physics and other scientific and technical areas, this new title offers elementary and thus easily accessible introductions to the various fields of research addressed in the book.

Data Preprocessing in Data Mining

Author : Salvador García
Publisher : Springer
ISBN 13 : 3319102478
Total Pages : 327 pages
Book Rating : 4.3/5 (191 download)

DOWNLOAD NOW!

Book Synopsis Data Preprocessing in Data Mining by : Salvador García

Download or read book Data Preprocessing in Data Mining written by Salvador García and published by Springer. This book was released on 2014-08-30 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Neural Networks and Statistical Learning

Author : Ke-Lin Du
Publisher : Springer Nature
ISBN 13 : 1447174526
Total Pages : 996 pages
Book Rating : 4.4/5 (471 download)

DOWNLOAD NOW!

Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du

Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Nature. This book was released on 2019-09-12 with total page 996 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Smart Applications and Data Analysis

Author : Mohamed Hamlich
Publisher : Springer Nature
ISBN 13 : 3030451836
Total Pages : 366 pages
Book Rating : 4.0/5 (34 download)

DOWNLOAD NOW!

Book Synopsis Smart Applications and Data Analysis by : Mohamed Hamlich

Download or read book Smart Applications and Data Analysis written by Mohamed Hamlich and published by Springer Nature. This book was released on 2020-06-04 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes refereed proceedings of the Third International Conference on Smart Applications and Data Analysis, SADASC 2020, held in Marrakesh, Morocco. Due to the COVID-19 pandemic the conference has been postponed to June 2020. The 24 full papers and 3 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers are organized according to the following topics: ontologies and meta modeling; cyber physical systems and block-chains; recommender systems; machine learning based applications; combinatorial optimization; simulations and deep learning.

Case Studies in Applied Bayesian Data Science

Author : Kerrie L. Mengersen
Publisher : Springer Nature
ISBN 13 : 3030425533
Total Pages : 415 pages
Book Rating : 4.0/5 (34 download)

DOWNLOAD NOW!

Book Synopsis Case Studies in Applied Bayesian Data Science by : Kerrie L. Mengersen

Download or read book Case Studies in Applied Bayesian Data Science written by Kerrie L. Mengersen and published by Springer Nature. This book was released on 2020-05-28 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.

Artificial Neural Networks - ICANN 2010

Author : Konstantinos Diamantaras
Publisher : Springer Science & Business Media
ISBN 13 : 3642158242
Total Pages : 591 pages
Book Rating : 4.6/5 (421 download)

DOWNLOAD NOW!

Book Synopsis Artificial Neural Networks - ICANN 2010 by : Konstantinos Diamantaras

Download or read book Artificial Neural Networks - ICANN 2010 written by Konstantinos Diamantaras and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three volume set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed proceedings of the 20th International Conference on Artificial Neural Networks, ICANN 2010, held in Thessaloniki, Greece, in September 20010. The 102 revised full papers, 68 short papers and 29 posters presented were carefully reviewed and selected from 241 submissions. The third volume is divided in topical sections on classification – pattern recognition, learning algorithms and systems, computational intelligence, IEM3 workshop, CVA workshop, and SOINN workshop.

Intelligent Data Engineering and Automated Learning – IDEAL 2020

Author : Cesar Analide
Publisher : Springer Nature
ISBN 13 : 3030623653
Total Pages : 633 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!

Book Synopsis Intelligent Data Engineering and Automated Learning – IDEAL 2020 by : Cesar Analide

Download or read book Intelligent Data Engineering and Automated Learning – IDEAL 2020 written by Cesar Analide and published by Springer Nature. This book was released on 2020-10-29 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.

Applications of Robotics in Industry Using Advanced Mechanisms

Author : Janmenjoy Nayak
Publisher : Springer Nature
ISBN 13 : 3030302717
Total Pages : 417 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Applications of Robotics in Industry Using Advanced Mechanisms by : Janmenjoy Nayak

Download or read book Applications of Robotics in Industry Using Advanced Mechanisms written by Janmenjoy Nayak and published by Springer Nature. This book was released on 2019-09-03 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shares important findings on the application of robotics in industry using advanced mechanisms, including software and hardware. It presents a collection of recent trends and research on various advanced computing paradigms such as soft computing, robotics, smart automation, power control, and uncertainty analysis. The book constitutes the proceedings of the 1st International Conference on Application of Robotics in Industry using Advanced Mechanisms (ARIAM2019), which offered a platform for sharing original research findings, presenting innovative ideas and applications, and comparing notes on various aspects of robotics. The contributions highlight the latest research and industrial applications of robotics, and discuss approaches to improving the smooth functioning of industries. Moreover, they focus on designing solutions for complex engineering problems and designing system components or processes to meet specific needs, with due considerations for public health and safety, including cultural, societal, and environmental considerations. Taken together, they offer a valuable resource for researchers, scientists, engineers, professionals and students alike.

Soft Computing for Biomedical Applications and Related Topics

Author : Vladik Kreinovich
Publisher : Springer Nature
ISBN 13 : 3030495361
Total Pages : 322 pages
Book Rating : 4.0/5 (34 download)

DOWNLOAD NOW!

Book Synopsis Soft Computing for Biomedical Applications and Related Topics by : Vladik Kreinovich

Download or read book Soft Computing for Biomedical Applications and Related Topics written by Vladik Kreinovich and published by Springer Nature. This book was released on 2020-06-29 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents innovative intelligent techniques, with an emphasis on their biomedical applications. Although many medical doctors are willing to share their knowledge – e.g. by incorporating it in computer-based advisory systems that can benefit other doctors – this knowledge is often expressed using imprecise (fuzzy) words from natural language such as “small,” which are difficult for computers to process. Accordingly, we need fuzzy techniques to handle such words. It is also desirable to extract general recommendations from the records of medical doctors’ decisions – by using machine learning techniques such as neural networks. The book describes state-of-the-art fuzzy, neural, and other techniques, especially those that are now being used, or potentially could be used, in biomedical applications. Accordingly, it will benefit all researchers and students interested in the latest developments, as well as practitioners who want to learn about new techniques.

Gene Expression Analysis

Author : Nalini Raghavachari
Publisher : Humana
ISBN 13 : 9781493978335
Total Pages : 0 pages
Book Rating : 4.9/5 (783 download)

DOWNLOAD NOW!

Book Synopsis Gene Expression Analysis by : Nalini Raghavachari

Download or read book Gene Expression Analysis written by Nalini Raghavachari and published by Humana. This book was released on 2018-05-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides experimental and bioinformatics approaches related to different aspects of gene expression analysis. Divided in three sections chapters detail wet-lab protocols, bioinformatics approaches, single-cell gene expression, highly multiplexed amplicon sequencing, multi-omics techniques, and targeted sequencing. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Gene Expression Analysis: Methods and Protocols aims provide useful information to researchers worldwide.

Explainable and Interpretable Models in Computer Vision and Machine Learning

Author : Hugo Jair Escalante
Publisher : Springer
ISBN 13 : 3319981315
Total Pages : 305 pages
Book Rating : 4.3/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Explainable and Interpretable Models in Computer Vision and Machine Learning by : Hugo Jair Escalante

Download or read book Explainable and Interpretable Models in Computer Vision and Machine Learning written by Hugo Jair Escalante and published by Springer. This book was released on 2018-11-29 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Exploration and Analysis of DNA Microarray and Other High-Dimensional Data

Author : Dhammika Amaratunga
Publisher : Wiley
ISBN 13 : 9781118364543
Total Pages : 344 pages
Book Rating : 4.3/5 (645 download)

DOWNLOAD NOW!

Book Synopsis Exploration and Analysis of DNA Microarray and Other High-Dimensional Data by : Dhammika Amaratunga

Download or read book Exploration and Analysis of DNA Microarray and Other High-Dimensional Data written by Dhammika Amaratunga and published by Wiley. This book was released on 2014-01-31 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition " ... extremely well written ... a comprehensive and up-to-date overview of this important field."--Journal of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to.

High-dimensional Microarray Data Analysis

Author : Shuichi Shinmura
Publisher : Springer
ISBN 13 : 9811359989
Total Pages : 437 pages
Book Rating : 4.8/5 (113 download)

DOWNLOAD NOW!

Book Synopsis High-dimensional Microarray Data Analysis by : Shuichi Shinmura

Download or read book High-dimensional Microarray Data Analysis written by Shuichi Shinmura and published by Springer. This book was released on 2019-05-14 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.

Advanced Analysis Of Gene Expression Microarray Data

Author : Aidong Zhang
Publisher : World Scientific Publishing Company
ISBN 13 : 9813106646
Total Pages : 356 pages
Book Rating : 4.8/5 (131 download)

DOWNLOAD NOW!

Book Synopsis Advanced Analysis Of Gene Expression Microarray Data by : Aidong Zhang

Download or read book Advanced Analysis Of Gene Expression Microarray Data written by Aidong Zhang and published by World Scientific Publishing Company. This book was released on 2006-06-27 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data.Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods:• Gene-based analysis: the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets• Sample-based analysis: supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described• Pattern-based analysis: methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed• Visualization tools: various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.

Analyzing Microarray Gene Expression Data

Author : Geoffrey J. McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 0471726125
Total Pages : 366 pages
Book Rating : 4.4/5 (717 download)

DOWNLOAD NOW!

Book Synopsis Analyzing Microarray Gene Expression Data by : Geoffrey J. McLachlan

Download or read book Analyzing Microarray Gene Expression Data written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2005-02-18 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists seeking to more effectively analyze their own experimental data, the text features a unique interdisciplinary approach and a combined academic and practical perspective that offers readers the most complete and applied coverage of the subject matter to date. Following a basic overview of the biological and technical principles behind microarray experimentation, the text provides a look at some of the most effective tools and procedures for achieving optimum reliability and reproducibility of research results, including: An in-depth account of the detection of genes that are differentially expressed across a number of classes of tissues Extensive coverage of both cluster analysis and discriminant analysis of microarray data and the growing applications of both methodologies A model-based approach to cluster analysis, with emphasis on the use of the EMMIX-GENE procedure for the clustering of tissue samples The latest data cleaning and normalization procedures The uses of microarray expression data for providing important prognostic information on the outcome of disease