Multi-Omics for the Understanding of Brain Diseases
Author:
Publisher:
Published: 2021-12-13
Total Pages: 204
ISBN-13: 9783036526027
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Author:
Publisher:
Published: 2021-12-13
Total Pages: 204
ISBN-13: 9783036526027
DOWNLOAD EBOOKAuthor: Min Tang
Publisher: Frontiers Media SA
Published: 2022-11-23
Total Pages: 224
ISBN-13: 2832506674
DOWNLOAD EBOOKAs 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: Andrea Legati
Publisher: Frontiers Media SA
Published: 2022-11-30
Total Pages: 165
ISBN-13: 2832507867
DOWNLOAD EBOOKAuthor: Manoj Kumar Jaiswal
Publisher: Frontiers Media SA
Published: 2022-08-03
Total Pages: 133
ISBN-13: 2889766918
DOWNLOAD EBOOKAuthor:
Publisher:
Published: 2021-12-13
Total Pages: 178
ISBN-13: 9783036525822
DOWNLOAD EBOOKAuthor: Jessica Ding
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKPhysiological functioning of an organ system such as healthy brain functioning is enabled by orchestrated information flow between molecular entities within cells, between cells, and across tissues and its understanding requires a systems approach. In complex disease such as Alzheimer's disease, there are numerous subtle, moderate, and strong perturbations across biological networks which result in disease onset and progression. High-throughput multi-omics profiling of genetic variants, the epigenome, transcriptome, proteome, metabolome, and microbiome is a first step in probing the complexity of biological systems and disease etiologies. There remains the problems of multi-omics integration, modeling and interpretation, and accessibility of computational analyses. To address these issues, in the methods development section of my dissertation research, I developed the complete overhaul of the web server for Mergeomics, a method for pathway- and network-level understanding of complex disease using multi-omics integration, which significantly improved the usability and functionality of the tool. To facilitate omics-driven drug discovery, I implemented the PharmOmics web server and the direct pipeline from Mergeomics, allowing users to gain immediate therapeutic insight from disease-associated pathways, key drivers, and key driver subnetworks. The second focus of my dissertation was the application of systems biology analytical tools to understand metabolic contributions to Alzheimer's disease. I determined hippocampal and hypothalamic cell type specific alterations in response to amyloid-beta accumulation in the 5XFAD mouse model and investigated the effect of metabolically challenging 5XFAD mice with fructose overconsumption and supplementation with docosahexaenoic acid and nicotinamide riboside. I identified cell type- and tissue-resolved pathway and network mechanisms of disease exacerbation by metabolic challenge and disease alleviation by docosahexaenoic acid and nicotinamide. My efforts in developing the web server implementations of Mergeomics and PharmOmics have promoted greater accessibility of deriving mechanistic insight from big biological data for the broader scientific community, and use of these tools have allowed unbiased examination into the connection between metabolic syndrome and Alzheimer's disease and uncovered potential therapeutic targets and strategies via dietary modulation.
Author: Shouneng Peng
Publisher: Frontiers Media SA
Published: 2024-03-13
Total Pages: 139
ISBN-13: 2832546323
DOWNLOAD EBOOKAuthor: Lucas Araujo Caldi Gomes
Publisher:
Published: 2020
Total Pages:
ISBN-13:
DOWNLOAD EBOOKParkinson's Disease (PD) is the second most prevalent and fastest-growing neurological disorder. The number of affected individuals is expected to double in the next 20 years. The exact molecular mechanisms underlying PD pathology are not completely understood. In addition, its diagnosis mainly relies on clinical criteria related to the characteristic motor dysfunction in PD. Since the symptoms only start to appear at advanced stages of the nigrostriatal degeneration characteristic in PD, there is a strong limitation for the promotion of therapeutic strategies that might be able to change t...
Author: Ting Jin
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKGene 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.
Author: Lucas Caldi Gomes
Publisher:
Published: 2020
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKParkinson's Disease (PD) is the second most prevalent and fastest-growing neurological disorder. The number of affected individuals is expected to double in the next 20 years. The exact molecular mechanisms underlying PD pathology are not completely understood. In addition, its diagnosis mainly relies on clinical criteria related to the characteristic motor dysfunction in PD. Since the symptoms only start to appear at advanced stages of the nigrostriatal degeneration characteristic in PD, there is a strong limitation for the promotion of therapeutic strategies that might be able to change t ...