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:

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:

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:

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

Download or read book Classification in BioApps written by Nilanjan Dey and published by Springer. This book was released on 2017-11-10 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges. Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization. In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.

Download or read book Machine Learning in Single Cell RNA seq Data Analysis written by Khalid Raza and published by Springer Nature. This book was released on with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Machine Learning Methods for Single cell RNA sequencing Data Analysis written by Chuanqi Wang and published by . This book was released on 2021 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Benchmarking Statistical and Machine Learning Methods for Single cell RNA Sequencing Data written by Nan Xi and published by . This book was released on 2021 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The large-scale, high-dimensional, and sparse single-cell RNA sequencing (scRNA-seq) data have raised great challenges in the pipeline of data analysis. A large number of statistical and machine learning methods have been developed to analyze scRNA-seq data and answer related scientific questions. Although different methods claim advantages in certain circumstances, it is difficult for users to select appropriate methods for their analysis tasks. Benchmark studies aim to provide recommendations for method selection based on an objective, accurate, and comprehensive comparison among cutting-edge methods. They can also offer suggestions for further methodological development through massive evaluations conducted on real data. In Chapter 2, we conduct the first, systematic benchmark study of nine cutting-edge computational doublet-detection methods. In scRNA-seq, doublets form when two cells are encapsulated into one reaction volume by chance. The existence of doublets, which appear as but are not real cells, is a key confounder in scRNA-seq data analysis. Computational methods have been developed to detect doublets in scRNA-seq data; however, the scRNA-seq field lacks a comprehensive benchmarking of these methods, making it difficult for researchers to choose an appropriate method for their specific analysis needs. Our benchmark study compares doublet-detection methods in terms of their detection accuracy under various experimental settings, impacts on downstream analyses, and computational efficiency. Our results show that existing methods exhibited diverse performance and distinct advantages in different aspects. In Chapter 3, we develop an R package DoubletCollection to integrate the installation and execution of different doublet-detection methods. Traditional benchmark studies can be quickly out-of-date due to their static design and the rapid growth of available methods. DoubletCollection addresses this issue in benchmarking doublet-detection methods for scRNA-seq data. DoubletCollection provides a unified interface to perform and visualize downstream analysis after doublet-detection. Additionally, we created a protocol using DoubletCollection to execute and benchmark doublet-detection methods. This protocol can automatically accommodate new doublet-detection methods in the fast-growing scRNA-seq field. In Chapter 4, we conduct the first comprehensive empirical study to explore the best modeling strategy for autoencoder-based imputation methods specific to scRNA-seq data. The autoencoder-based imputation method is a family of promising methods to denoise sparse scRNA-seq data; however, the design of autoencoders has not been formally discussed in the literature. Current autoencoder-based imputation methods either borrow the practice from other fields or design the model on an ad hoc basis. We find that the method performance is sensitive to the key hyperparameter of autoencoders, including architecture, activation function, and regularization. Their optimal settings on scRNA-seq are largely different from those on other data types. Our results emphasize the importance of exploring hyperparameter space in such complex and flexible methods. Our work also points out the future direction of improving current methods.

Download or read book Special Topics in Information Technology written by Luigi Piroddi and published by Springer Nature. This book was released on 2022-01-01 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

Download or read book Machine Learning in Single Cell RNA seq Data Analysis written by Khalid Raza and published by Springer. This book was released on 2024-09-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets.

Download or read book Introduction to Machine Learning and Bioinformatics written by Sushmita Mitra and published by CRC Press. This book was released on 2008-06-05 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Download or read book Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine written by Tao Zeng and published by Frontiers Media SA. This book was released on 2020-03-30 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning Methodologies To Study Molecular Interactions written by Elif Ozkirimli and published by Frontiers Media SA. This book was released on 2022-01-21 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. Elif Ozkirimli is a full time employee of F. Hoffmann-La Roche AG, Switzerland and Dr. Artur Yakimovich is a full time employee of Roche Products Limited, UK. All other Topic Editors declare no competing interests with regards to the Research Topic.

Download or read book Machine Learning Methods for Multi Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer Nature. This book was released on 2023-12-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.