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Advanced Interpretable Machine Learning Methods For Clinical Ngs Big Data Of Complex Hereditary Diseases Volume Ii
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Book Synopsis Advanced interpretable machine learning methods for clinical NGS big data of complex hereditary diseases – volume II by : Yudong Cai
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:
Book Synopsis Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases, 2nd Edition by : Yudong Cai
Download or read book Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases, 2nd Edition written by Yudong Cai and published by Frontiers Media SA. This book was released on 2021-07-01 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher’s note: This is a 2nd edition due to an article retraction
Book Synopsis Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases by : Yudong Cai
Download or read book Advanced Interpretable Machine Learning Methods for Clinical NGS Big Data of Complex Hereditary Diseases written by Yudong Cai and published by . This book was released on 2020 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Book Synopsis Big Data in Omics and Imaging by : Momiao Xiong
Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2017-12-01 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.
Book Synopsis Clinical Applications for Next-Generation Sequencing by : Urszula Demkow
Download or read book Clinical Applications for Next-Generation Sequencing written by Urszula Demkow and published by Academic Press. This book was released on 2015-09-10 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical Applications for Next Generation Sequencing provides readers with an outstanding postgraduate resource to learn about the translational use of NGS in clinical environments. Rooted in both medical genetics and clinical medicine, the book fills the gap between state-of-the-art technology and evidence-based practice, providing an educational opportunity for users to advance patient care by transferring NGS to the needs of real-world patients. The book builds an interface between genetic laboratory staff and clinical health workers to not only improve communication, but also strengthen cooperation. Users will find valuable tactics they can use to build a systematic framework for understanding the role of NGS testing in both common and rare diseases and conditions, from prenatal care, like chromosomal abnormalities, up to advanced age problems like dementia. - Fills the gap between state-of-the-art technology and evidence-based practice - Provides an educational opportunity which advances patient care through the transfer of NGS to real-world patient assessment - Promotes a practical tool that clinicians can apply directly to patient care - Includes a systematic framework for understanding the role of NGS testing in many common and rare diseases - Presents evidence regarding the important role of NGS in current diagnostic strategies
Book Synopsis Handbook of Machine Learning Applications for Genomics by : Sanjiban Sekhar Roy
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.
Book Synopsis Big Data in Omics and Imaging by : Momiao Xiong
Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2018-06-14 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Book Synopsis Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine by : Tao Zeng
Download or read book Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine written by Tao Zeng and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision medicine is being developed as a preventative, diagnostic and treatment tool to combat complex human diseases in a personalized manner. By utilizing high-throughput technologies, dynamic 'omics data including genetics, epi-genetics and even meta-genomics has produced temporal-spatial big biological datasets which can be associated with individual genotypes underlying pathogen progressive phenotypes. It is therefore necessary to investigate how to integrate these multi-scale 'omics datasets to distinguish the novel individual-specific disease causes from conventional cohort-common disease causes. Currently, machine learning plays an important role in biological and biomedical research, especially in the analysis of big 'omics data. However, in contrast to traditional big social data, 'omics datasets are currently always "small-sample-high-dimension", which causes overwhelming application problems and also introduces new challenges: (1) Big 'omics datasets can be extremely unbalanced, due to the difficulty of obtaining enough positive samples of such rare mutations or rare diseases; (2) A large number of machine learning models are "black box," which is enough to apply in social applications. However, in biological or biomedical fields, knowledge of the molecular mechanisms underlying any disease or biological study is necessary to deepen our understanding; (3) The genotype-phenotype association is a "white clue" captured in conventional big data studies. But identification of "causality" rather than association would be more helpful for physicians or biologists, as this can be used to determine an experimental target as the subject of future research. Therefore, to simultaneously improve the phenotype discrimination and genotype interpretability for complex diseases, it is necessary: To design and implement new machine learning technologies to integrate prior-knowledge with new 'omics datasets to provide transferable learning methods by combining multiple sources of data; To develop new network-based theories and methods to balance the trade-off between accuracy and interpretability of machine learning in biomedical and biological domains; To enhance the causality inference on "small-sample high dimension" data to capture the personalized causal relationship.
Book Synopsis Big Data Analytics in Genomics by : Ka-Chun Wong
Download or read book Big Data Analytics in Genomics written by Ka-Chun Wong and published by Springer. This book was released on 2016-10-24 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.
Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Book Synopsis Computational Methods for Next Generation Sequencing Data Analysis by : Ion Mandoiu
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.
Book Synopsis Patterns in Big Data Bioinformatics by : Mateusz Garbulowski
Download or read book Patterns in Big Data Bioinformatics written by Mateusz Garbulowski and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Systems Genetics of Human Complex Diseases - Volume II by : Guiyou Liu
Download or read book Systems Genetics of Human Complex Diseases - Volume II written by Guiyou Liu and published by Frontiers Media SA. This book was released on 2022-08-19 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Clinical DNA Variant Interpretation by : Conxi Lázaro
Download or read book Clinical DNA Variant Interpretation written by Conxi Lázaro and published by Elsevier. This book was released on 2021-03-02 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live in a very exciting time in the field of human genetics; NGS technology is generating enormous amounts of data for classifying genetic variants. However, defining the role of these variants in human health and disease can be difficult, especially when a variant's mechanism of action or the phenotype associated with a genetic mutation are not well defined. Work in multidisciplinary teams and development of multifactorial algorithms, including artificial intelligence and machine learning approaches, are integral to streamlining variant classification. Clinical DNA Variant Interpretation: Theory and Practice, a new volume in the Translational and Applied Genomics series, brings more than thirty international experts together to compile variant interpretation best practices and approaches in a single volume, covering foundational aspects, modes of analysis, technology, disease and disorder specific case studies, and clinical integration. This book provides a deep theoretical background, as well as applied case studies and methodology, enabling researchers, clinicians, and healthcare providers to effectively classify DNA variants associated with disease and patient phenotypes. Practical chapters discuss genomic variant interpretation, terminology and nomenclature; international consensus guidelines; population allele frequency; functional evidence transcripts for RNA, proteins, and enzymes; somatic mutations and somatic profiling; CNV interpretation; quantitative modeling; machine learning approaches; genomic data sharing; genetic testing in clinical practice; and holistic case-level interpretation. Biomedical specialties of relevance include internal medicine, medical genetics, oncology, psychiatry, neurology, and immunology, and those driving implementation of precision medicine and personalized treatments.
Book Synopsis Biologically Interpretable Machine Learning Methods to Understand Gene Regulation for Disease Phenotypes by : Ting Jin
Download or read book Biologically Interpretable Machine Learning Methods to Understand Gene Regulation for Disease Phenotypes written by Ting Jin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene expression and regulation is a key molecular mechanism driving the development of human diseases, particularly at the cell type level, but it remains elusive. For example in many brain diseases, such as Alzheimer's disease (AD), understanding how cell-type gene expression and regulation change across multiple stages of AD progression is still challenging. Moreover, interindividual variability of gene expression and regulation is a known characteristic of the human brain and brain diseases. However, it is still unclear how interindividual variability affects personalized gene regulation in brain diseases including AD, thereby contributing to their heterogeneity. Recent technological advances have enabled the detection of gene regulation activities through multi-omics (i.e., genomics, transcriptomics, epigenomics, proteomics). In particular, emerging single-cell sequencing technologies (e.g., scRNA-seq, scATAC-seq) allow us to study functional genomics and gene regulation at the cell-type level. Moreover, these multi-omics data of populations (e.g., human individuals) provide a unique opportunity to study the underlying regulatory mechanisms occurring in brain disease progression and clinical phenotypes. For instance, PsychAD is a large project generating single-cell multi-omics data including many neuronal and glial cell types, aiming to understand the molecular mechanisms of neuropsychiatric symptoms of multiple brain diseases (e.g., AD, SCZ, ASD, Bipolar) from over 1,000 individuals. However, analyzing and integrating large-scale multi-omics data at the population level, as well as understanding the mechanisms of gene regulation, also remains a challenge. Machine learning is a powerful and emerging tool to decode the unique complexities and heterogeneity of human diseases. For instance, Beebe-Wang, Nicosia, et al. developed MD-AD, a multi-task neural network model to predict various disease phenotypes in AD patients using RNA-seq. Additionally, with advancements in graph neural networks, which possess enhanced capabilities to represent sophisticated gene network structures like gene regulation networks that control gene expression. Efforts have also been made to capture the gene regulation heterogeneity of brain diseases. For instance, Kim SY has applied graph convolutional networks to offer personalized diagnostic insights through population graphs that correspond with disease progression. However, many existing machine learning methods are often limited to constructing accurate models for disease phenotype prediction and frequently lack biological interpretability or personalized insights, especially in gene regulation. Therefore, to address these challenges, my Ph.D. works have developed three machine-learning methods designed to decode the gene regulation mechanisms of human diseases. First, in this dissertation, I will present scGRNom, a computational pipeline that integrates multi-omic data to construct cell-type gene regulatory networks (GRNs) linking non-coding regulatory elements. Next, I will introduce i-BrainMap an interpretable knowledge-guided graph neural network model to prioritize personalized cell type disease genes, regulatory linkages, and modules. Thirdly, I introduce ECMaker, a semi-restricted Boltzmann machine (semi-RBM) method for identifying gene networks to predict diseases and clinical phenotypes. Overall, all our interpretable machine learning models improve phenotype prediction, prioritize key genes and networks associated with disease phenotypes, and are further aimed at enhancing our understanding of gene regulatory mechanisms driving disease progression and clinical phenotypes.
Book Synopsis Landscape of Next Generation Sequencing Using Pattern Recognition by : Saurav Mallik
Download or read book Landscape of Next Generation Sequencing Using Pattern Recognition written by Saurav Mallik and published by CRC Press. This book was released on 2024-10-23 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on an eminent technology called next generation sequencing (NGS) which has entirely changed the procedure of examining organisms and will have a great impact on biomedical research and disease diagnosis. Numerous computational challenges have been brought on by the rapid advancement of large-scale next-generation sequencing (NGS) technologies and their application. The term ""biomedical imaging"" refers to the use of a variety of imaging techniques (such as X-rays, CT scans, MRIs, ultrasounds, etc.) to get images of the interior organs of a human being for potential diagnostic, treatment planning, follow-up, and surgical purposes. In these circumstances, deep learning, a new learning method that uses multi-layered artificial neural networks (ANNs) for unsupervised, supervised, and semi-supervised learning, has attracted a lot of interest for applications to NGS and imaging, even when both of these data are used for the same group of patients. The three main research phenomena in biomedical research are disease classification, feature dimension reduction, and heterogeneity. AI approaches are used by clinical researchers to efficiently analyse extremely complicated biomedical datasets (e.g., multi-omic datasets. With the use of NGS data and biomedical imaging of various human organs, researchers may predict diseases using a variety of deep learning models. Unparalleled prospects to improve the work of radiologists, clinicians, and biomedical researchers, speed up disease detection and diagnosis, reduce treatment costs, and improve public health are presented by using deep learning models in disease prediction using NGS and biomedical imaging. This book influences a variety of critical disease data and medical images.
Book Synopsis Machine Learning Methods for Multi-Omics Data Integration by : Abedalrhman Alkhateeb
Download or read book Machine Learning Methods for Multi-Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer. This book was released on 2023-11-14 with total page 0 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.
