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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.

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

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.

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.

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.

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.

It is an incredible feat of nature that the same genome contains the code to every cell in each living organism. From this same genome, each unique cell type gains a different program of gene expression that enables the development and function of an organism throughout its lifespan. The non-coding genome - the ~98 of the genome that does not code directly for proteins - serves an important role in generating the diverse programs of gene expression turned on in each unique cell state. A complex network of proteins bind specific regulatory elements in the non-coding genome to regulate the expression of nearby genes. While basic principles of gene regulation are understood, the regulatory code of which factors bind together at which genomic elements to turn on which genes remains to be revealed. Further, we do not understand how disruptions in gene regulation, such as from mutations that fall in non-coding regions, ultimately lead to disease or other changes in cell state. In this work we present several methods developed and applied to learn the regulatory code or the rules that govern non-coding regions of the genome and how they regulate nearby genes. We first formulate the problem as one of learning pairs of sequence motifs and expressed regulator proteins that jointly predict the state of the cell, such as the cell type specific gene expression or chromatin accessibility. Using pre-engineered sequence features and known expression, we use a paired-feature boosting approach to build an interpretable model of how the non-coding genome contributes to cell state. We also demonstrate a novel improvement to this method that takes into account similarities between closely related cell types by using a hierarchy imposed on all of the predicted cell states. We apply this method to discover validated regulators of tadpole tail regeneration and to predict protein-ligand binding interactions. Recognizing the need for improved sequence features and stronger predictive performance, we then move to a deep learning modeling framework to predict epigenomic phenotypes such as chromatin accessibility from just underlying DNA sequence. We use deep learning models, specifically multi-task convolutional neural networks, to learn a featurization of sequences over several kilobases long and their mapping to a functional phenotype. We develop novel architectures that encode principles of genomics in models typically designed for computer vision, such as incorporating reverse complementation and the 3D structure of the genome. We also develop methods to interpret traditionally ``black box" neural networks by 1) assigning importance scores to each input sequence to the model, 2) summarizing non-redundant patterns learned by the model that are predictive in each cell type, and 3) discovering interactions learned by the model that provide indications as to how different non-coding sequence features depend on each other. We apply these methods in the system of hematopoiesis to interpret chromatin dynamics across differentiation of blood cell types, to understand immune stimulation, and to interpret immune disease-associated variants that fall in non-coding regions. We demonstrate strong performance of our boosting and deep learning models and demonstrate improved performance of these machine learning frameworks when taking into account existing knowledge about the biological system being modeled. We benchmark our interpretation methods using gold standard systems and existing experimental data where available. We confirm existing knowledge surrounding essential factors in hematopoiesis, and also generate novel hypotheses surrounding how factors interact to regulate differentiation. Ultimately our work provides a set of tools for researchers to probe and understand the non-coding genome and its role in controlling gene expression as well as a set of novel insights surrounding how hematopoiesis is controlled on many scales from global quantification of regulatory sequence to interpretation of individual variants.