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Author : Lihong Peng
Publisher : Frontiers Media SA
ISBN 13 : 2832510345
Total Pages : 164 pages
Book Rating : 4.8/5 (325 download)
Download or read book Machine learning-based methods for RNA data analysis, volume II written by Lihong Peng and published by Frontiers Media SA. This book was released on 2023-01-02 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lihong Peng
Publisher : Frontiers Media SA
ISBN 13 : 2832514901
Total Pages : 134 pages
Book Rating : 4.8/5 (325 download)
Download or read book Machine learning-based methods for RNA data analysis - volume III written by Lihong Peng and published by Frontiers Media SA. This book was released on 2023-02-17 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lihong Peng
Publisher : Frontiers Media SA
ISBN 13 : 2889763846
Total Pages : 124 pages
Book Rating : 4.8/5 (897 download)
Download or read book Machine Learning-Based Methods for RNA Data Analysis written by Lihong Peng and published by Frontiers Media SA. This book was released on 2022-06-16 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Pankaj Barah
Publisher : CRC Press
ISBN 13 : 1000425738
Total Pages : 379 pages
Book Rating : 4.0/5 (4 download)
Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-21 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences
Author : Quan Zou
Publisher : Frontiers Media SA
ISBN 13 : 2889766322
Total Pages : 264 pages
Book Rating : 4.8/5 (897 download)
Download or read book Machine Learning Techniques on Gene Function Prediction Volume II written by Quan Zou and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Setubal
Publisher : Frontiers Media SA
ISBN 13 : 2832506402
Total Pages : 223 pages
Book Rating : 4.8/5 (325 download)
Download or read book Computational methods for microbiome analysis, volume 2 written by Setubal and published by Frontiers Media SA. This book was released on 2023-01-04 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Liang Cheng
Publisher : Frontiers Media SA
ISBN 13 : 2889769151
Total Pages : 158 pages
Book Rating : 4.8/5 (897 download)
Download or read book System Biology Methods and Tools for Integrating Omics Data - Volume II written by Liang Cheng and published by Frontiers Media SA. This book was released on 2022-09-07 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ion Mandoiu
Publisher : John Wiley & Sons
ISBN 13 : 1119272165
Total Pages : 464 pages
Book Rating : 4.1/5 (192 download)
Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-09-12 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.
Author : De-Shuang Huang
Publisher : Springer Nature
ISBN 13 : 3030608026
Total Pages : 638 pages
Book Rating : 4.0/5 (36 download)
Download or read book Intelligent Computing Theories and Application written by De-Shuang Huang and published by Springer Nature. This book was released on 2020-10-13 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12463 and LNCS 12464 constitutes - in conjunction with the volume LNAI 12465 - the refereed proceedings of the 16th International Conference on Intelligent Computing, ICIC 2020, held in Bari, Italy, in October 2020. The 162 full papers of the three proceedings volumes were carefully reviewed and selected from 457 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, addressing theories, methodologies, and applications in science and technology.
Author : Yudong Cai
Publisher : Frontiers Media SA
ISBN 13 : 2832514464
Total Pages : 194 pages
Book Rating : 4.8/5 (325 download)
Download or read book Advanced interpretable machine learning methods for clinical NGS big data of complex hereditary diseases – volume II written by Yudong Cai and published by Frontiers Media SA. This book was released on 2023-02-13 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Stephen Winters-Hilt
Publisher : Lulu.com
ISBN 13 : 1257645250
Total Pages : 436 pages
Book Rating : 4.2/5 (576 download)
Download or read book MacHine-Learning Based Sequence Analysis, Bioinformatics and Nanopore Transduction Detection written by Stephen Winters-Hilt and published by Lulu.com. This book was released on 2011-05-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.
Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 462 pages
Book Rating : 4.4/5 (987 download)
Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Author : Rabinarayan Satpathy
Publisher : John Wiley & Sons
ISBN 13 : 1119785618
Total Pages : 544 pages
Book Rating : 4.1/5 (197 download)
Download or read book Data Analytics in Bioinformatics written by Rabinarayan Satpathy and published by John Wiley & Sons. This book was released on 2021-01-20 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Author : Sanjiban Sekhar Roy
Publisher : Springer Nature
ISBN 13 : 9811691584
Total Pages : 222 pages
Book Rating : 4.8/5 (116 download)
Download or read book Handbook of Machine Learning Applications for Genomics written by Sanjiban Sekhar Roy and published by Springer Nature. This book was released on 2022-06-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Author : Quan Zou
Publisher : Frontiers Media SA
ISBN 13 : 2889632148
Total Pages : 485 pages
Book Rating : 4.8/5 (896 download)
Download or read book Machine Learning Techniques on Gene Function Prediction written by Quan Zou and published by Frontiers Media SA. This book was released on 2019-12-04 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ignacio Rojas
Publisher : Springer Nature
ISBN 13 : 3031078020
Total Pages : 485 pages
Book Rating : 4.0/5 (31 download)
Download or read book Bioinformatics and Biomedical Engineering written by Ignacio Rojas and published by Springer Nature. This book was released on 2022-06-07 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 9th International Work-Conference on IWBBIO 2020, held in Maspalomas, Gran Canaria, Spain, in June 2022. The total of 75 papers presented in the proceedings, was carefully reviewed and selected from 212 submissions. The papers cover the latest ideas and realizations in the foundations, theory, models, and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, biology, bioinformatics, and biomedicine.
Author : Jianyi Zhang
Publisher : Frontiers Media SA
ISBN 13 : 2832545262
Total Pages : 234 pages
Book Rating : 4.8/5 (325 download)
Download or read book Bioengineering and Biotechnology Approaches in Cardiovascular Regenerative Medicine, Volume II written by Jianyi Zhang and published by Frontiers Media SA. This book was released on 2024-02-26 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Research Topic is Volume II of a series. The previous volume, which has attracted over 40,000 views can be found here: Bioengineering and Biotechnology Approaches in Cardiovascular Regenerative MedicineCardiovascular diseases continue to be the leading cause of death while available clinical interventions have limited contributions to heart repair and regeneration. Cardiovascular regenerative medicine, characterized by a unique integration of biology, physical sciences, and bioengineering principles, has emerged as one of the most promising fields of translational research to regenerate the adult human heart.
