Multi-Omics for the Understanding of Brain Diseases PDF Download

Author:
Publisher:
ISBN: 9783036526027
Category : Science
Languages : en
Pages : 204

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Author: Andrea Legati
Publisher: Frontiers Media SA
ISBN: 2832507867
Category : Science
Languages : en
Pages : 165

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Author: Min Tang
Publisher: Frontiers Media SA
ISBN: 2832506674
Category : Science
Languages : en
Pages : 224

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Book Description
As the cost of high-throughput sequencing goes down, huge volumes of biological and medical data have been produced from various sequencing platforms at multiple molecular levels including genome, transcriptome, proteome, epigenome, metabolome, and so on. For a long time, data analysis on single molecular levels has paved the way to answer many important research questions. However, many Aging-Related Neuronal Diseases (ARNDs) and Central Nervous System (CNS) aging involve interactions of molecules from multiple molecular levels, in which conclusions based on single molecular levels are usually incomplete and sometimes misleading. In these scenarios, multi-omics data analysis has unprecedentedly helped capture much more useful information for the diagnosis, treatment, prognosis, and drug discovery of ARNDs. The first step towards a multi-omics analysis is to establish reliable and robust multi-omics datasets. In the past years, a few important ARNDs-associated multi-omics databases like Allen Brain have been constructed, which raised immediate needs like data curation, normalization, interpretation, and visualization for integrative multi-omics explorations. Though there have been several well-established multi-omics databases for ARNDs like Alzheimer’s disease, similar databases for other ARNDs are still in urgent need. After the databases establish, many computational tools and experiential strategies should be developed specifically for them. First, the multi-omics data are usually extremely noisy, complex, heterogeneous and in high dimension, which presents the need for appropriate denoising and dimension reduction methods. Second, since the multi-omics and non-omics data like pathological and clinical data are usually in different data spaces, a useful algorithm to mapping them into the same data space and integrate them is nontrivial. In the multi-omics era, there are numerous data-centric tools for the integration of multi-omics datasets, which could be generally divided into three categories: unsupervised, supervised, and semi-supervised methods. Commonly used algorithms include but not limited to Bayesian-based methods, Network-based methods, multi-step analysis methods, and multiple kernel learning methods. Third, methods are needed in studying and verifying the association between two or more levels of multi-omics data and non-omics data. For example, expression quantitative trait loci (eQTL) analysis is widely used to infer the association between a single nucleotide polymorphism (SNP) and the expression of a gene. Recently, the association between omics data and more complex data like pathological and clinical imaging data has been a hot research topic. The outcomes may reveal the underlying molecular mechanism and promote de novo drug design as well as drug repurposing for ARNDs. Here, we welcome investigators to share their Original Research, Review, Mini Review, Hypothesis and Theory, Perspective, Conceptual Analysis, Data Report, Brief Research Report, Code related to multi-omics studies of ARNDs, which can be applied for better diagnosis, treatment, prognosis and drug discovery of human diseases in the future era of precision medicine. Potential contents include but are not limited to the following: ▪ Methods for integrating, interpreting, or visualizing two or more omics data. ▪ Methods for identifying interactions between different data modalities. ▪ Methods for disease subtyping, biomarker prediction. ▪ Machine learning or deep learning methods on dimensional reduction and feature selection for big noisy data. ▪ Methods for studying the association among different omics data or between omics and non-omics data like clinical, pathological, and imaging data. ▪ Review of multi-omics resource about ARNDs and/or CNS aging. ▪ Experimental validation of biomarkers identified from multi-omics data analysis. ▪ Disease diagnosis and prognosis prediction from imaging and non-imaging data analysis, or both. ▪ Clinical applications or validations of findings from multi-omics data analysis.

Author: Francois Boller
Publisher: Springer Science & Business Media
ISBN: 3642467768
Category : Medical
Languages : en
Pages : 205

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The term "Alzheimer's disease" is currently used to refer to senile and also presenile dementia, but the heterogeneity of this disorder is demonstrated in many of its aspects. This is of great theoretical interest, and with the appearance of new therapeutic interventions, it may well also start to have very significant practical importance. To shed some light on the debate, the Fondation Ipsen organized an international symposium which took place on April 6, 1992. This volume contains the proceedings of this meeting, which was attended by researchers in epidemiology, clinical neurology and geriatrics, neuropsychology, neuropathology, molecular biology, and genetics.

Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309224187
Category : Science
Languages : en
Pages : 354

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Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

Author: Elena Aikawa
Publisher: BoD – Books on Demand
ISBN: 9535111507
Category : Medical
Languages : en
Pages : 544

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Due to population aging, calcific aortic valve disease (CAVD) has become the most common heart valve disease in Western countries. No therapies exist to slow this disease progression, and surgical valve replacement is the only effective treatment. Calcific Aortic Valve Disease covers the contemporary understanding of basic valve biology and the mechanisms of CAVD, provides novel insights into the genetics, proteomics, and metabolomics of CAVD, depicts new strategies in heart valve tissue engineering and regenerative medicine, and explores current treatment approaches. As we are on the verge of understanding the mechanisms of CAVD, we hope that this book will enable readers to comprehend our current knowledge and focus on the possibility of preventing disease progression in the future.

Author: Sara Mole
Publisher: OUP Oxford
ISBN: 019101558X
Category : Medical
Languages : en
Pages : 480

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The neuronal ceroid lipofuscinoses are an extremely rare group of inherited neurodegenerative diseases that primarily affect children. Core symptoms of these conditions typically include epilepsy, cognitive decline and visual failure. These diseases are so rare that professionals who come into contact with them need a consultative reference work that enables them to become expert, or identify who to contact for more details. Fully updated and revised, this second edition continues to be the definitive volume on this devastating group of disorders. Written by an international collection of authorities in the field, it provides invaluable advice on their diagnosis, patient care, and new treatments that are available. This new edition of the definitive reference text on the neuronal ceroid lipofuscinoses will prove useful for clinicians, family physicians, research scientists, diagnostic laboratories, families affected by the disease as well as by workers in industry planning translational research.

Author: Jyoti Sharma
Publisher: Frontiers Media SA
ISBN: 2889661253
Category : Science
Languages : en
Pages : 154

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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.

Author: Maria Teresa Ferretti
Publisher: Academic Press
ISBN: 012819345X
Category : Medical
Languages : en
Pages : 514

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Sex and Gender Differences in Alzheimer’s Disease: The Women’s Brain Project offers for the first time a critical overview of the evidence documenting sex and gender differences in Alzheimer’s disease neurobiology, biomarkers, clinical presentation, treatment, clinical trials and their outcomes, and socioeconomic impact on both patients and caregivers. This knowledge is crucial for clinical development, digital health solutions, as well as social and psychological support to Alzheimer’s disease families, in the frame of a precision medicine approach to Alzheimer’s disease.This book brings together up-to-date findings from a variety of experts, covering basic neuroscience, epidemiology, diagnosis, treatment, clinical trials development, socioeconomic factors, and psychosocial support. Alzheimer’s disease, the most common form of dementia, remains an unmet medical need for the planet. Wide interpersonal variability in disease onset, presentation, and biomarker profile make Alzheimer’s a clinical challenge to neuroscientists, clinicians, and drug developers alike, resulting in huge management costs for health systems and society. Not only do women represent the majority of Alzheimer’s disease patients, but they also represent two-thirds of caregivers. Understanding sex and gender differences in Alzheimer’s disease will lead to novel insights into disease mechanisms, and will be crucial for personalized disease management strategies and solutions, involving both the patient and their family. Endorsements/Reviews: "There is a clear sex and gender gap in outcomes for brain health disorders like Alzheimer’s disease, with strikingly negative outcomes for women. This understanding calls for a more systematic way of approaching this issue of inequality. This book effectively highlights and frames inequalities in all areas across the translational spectrum from bench-to-bedside and from boardroom-to-policy and economics. Closing the Brain Health Gap will help economies create recovery and prepare our systems for future global shocks." Harris A. Eyre MBBS, PhD, co-lead, Neuroscience-inspired Policy Initiative, OECD and PRODEO Institute. Instructor in Brain Health Diplomacy, Global Brain Health Institute, UCSF and TCD. "Sex and Gender Differences in Alzheimer's disease is the most important title to emerge on Alzheimer's disease in recent years.This comprehensive, multidisciplinary book is a must read for anyone with a serious interest in dementia prevention, diagnosis, treatment, care, cure and research. Precision medicine is the future of healthcare and this book represents an incredible and necessary resource to guide practice, policy and research in light of the fact that Alzheimer's disease disproportionately affects women. The combination of contributions from the most eminent experts and the most up-to-date research makes this an invaluable resource for clinicians, care providers, academics, researchers and policy makers. Given the complex nature of dementia and the multiple factors that influence risk and disease trajectory the scope of the book is both impressive and important covering sex differences in neurobiological processes, sex and gender differences in clinical aspects and gender differences linked to socioeconomic factors relevant to Alzheimer's disease. If you work in Alzheimer's disease, or indeed other dementias, then Sex and Gender Differences in Alzheimer's disease is a must have for your bookshelf." -- Sabina Brennan, PhD., C.Psychol.,PsSI., National representative for Ireland on Alzheimer Disease International's Medical and Scientific Advisory Panel

Author: Ting Jin
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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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.