Download Full eBooks in PDF
Graph Embedding Methods For Multiple Omics Data Analysis Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Graph Embedding Methods For Multiple Omics Data Analysis book. This book definitely worth reading, it is an incredibly well-written.
Author : Chen Qingfeng,Wei Lan,Yi-Ping Phoebe Chen,Wilson Wen Bin Goh
Publisher : Frontiers Media SA
Page : 220 pages
File Size : 49,5 Mb
Release : 2021-11-08
Category : Science
ISBN : 9782889716005
Author : Haiqin Yang,Kitsuchart Pasupa,Andrew Chi-Sing Leung,James T. Kwok,Jonathan H. Chan,Irwin King
Publisher : Springer Nature
Page : 866 pages
File Size : 44,9 Mb
Release : 2020-11-18
Category : Computers
ISBN : 9783030638238
The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic.
Author : Ornella Cominetti,Sergio Oller Moreno,Sumeet Agarwal
Publisher : Frontiers Media SA
Page : 184 pages
File Size : 51,7 Mb
Release : 2023-05-12
Category : Science
ISBN : 9782832523421
Author : Kang Ning
Publisher : Springer Nature
Page : 173 pages
File Size : 43,5 Mb
Release : 2023-01-15
Category : Medical
ISBN : 9789811982101
This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.
Author : Jianing Xi,Zhenhua Yu
Publisher : Frontiers Media SA
Page : 109 pages
File Size : 44,8 Mb
Release : 2022-01-05
Category : Science
ISBN : 9782889719679
Author : Liang Cheng,Lei Deng,Mingxiang Teng
Publisher : Frontiers Media SA
Page : 158 pages
File Size : 45,8 Mb
Release : 2022-09-07
Category : Science
ISBN : 9782889769155
Author : De-Shuang Huang,Prashan Premaratne,Baohua Jin,Boyang Qu,Kang-Hyun Jo,Abir Hussain
Publisher : Springer Nature
Page : 823 pages
File Size : 47,5 Mb
Release : 2023-07-30
Category : Computers
ISBN : 9789819947614
This three-volume set of LNCS 14086, LNCS 14087 and LNCS 14088 constitutes - in conjunction with the double-volume set LNAI 14089-14090- the refereed proceedings of the 19th International Conference on Intelligent Computing, ICIC 2023, held in Zhengzhou, China, in August 2023. The 337 full papers of the three proceedings volumes were carefully reviewed and selected from 828 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
Author : Angelo Facchiano,Margherita Mutarelli,Dominik Heider
Publisher : Frontiers Media SA
Page : 160 pages
File Size : 43,7 Mb
Release : 2024-02-07
Category : Science
ISBN : 9782832544457
Author : Liang Cheng,Lei Deng,Chuan-Xing Li,Yan Zhang,Mingxiang Teng
Publisher : Frontiers Media SA
Page : 793 pages
File Size : 51,8 Mb
Release : 2022-11-29
Category : Science
ISBN : 9782832507384
Author : Gary Hardiman
Publisher : MDPI
Page : 202 pages
File Size : 53,7 Mb
Release : 2020-04-15
Category : Science
ISBN : 9783039287444
A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.
Author : Debmalya Barh,Vasco Azevedo
Publisher : Academic Press
Page : 490 pages
File Size : 48,7 Mb
Release : 2019-06-06
Category : Business & Economics
ISBN : 9780128149201
Single-Cell Omics: Volume 1: Technological Advances and Applications provides the latest technological developments and applications of single-cell technologies in the field of biomedicine. In the current era of precision medicine, the single-cell omics technology is highly promising due to its potential in diagnosis, prognosis and therapeutics. Sections in the book cover single-cell omics research and applications, diverse technologies applied in the topic, such as pangenomics, metabolomics, and multi-omics of single cells, data analysis, and several applications of single-cell omics within the biomedical field, for example in cancer, metabolic and neuro diseases, immunology, pharmacogenomics, personalized medicine and reproductive health. This book is a valuable source for bioinformaticians, molecular diagnostic researchers, clinicians and members of the biomedical field who are interested in understanding more about single-cell omics and its potential for research and diagnosis. Covers not only the technological aspects, but also the diverse applications of single cell omics in the biomedical field Summarizes the latest progress in single cell omics and discusses potential future developments for research and diagnosis Written by experts across the world, bringing different points-of-view and case studies to give a comprehensive overview on the topic
Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 52,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885
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.
Author : Momiao Xiong
Publisher : CRC Press
Page : 400 pages
File Size : 47,7 Mb
Release : 2018-06-14
Category : Mathematics
ISBN : 9781351172622
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.
Author : Geng Chen,Xingdong Chen,Rongshan Yu,Zhichao Liu
Publisher : Frontiers Media SA
Page : 189 pages
File Size : 48,7 Mb
Release : 2023-03-13
Category : Science
ISBN : 9782832513323
Author : Russ B Altman,A Keith Dunker,Lawrence Hunter,Marylyn D Ritchie,Tiffany A Murray,Teri E Klein
Publisher : World Scientific
Page : 764 pages
File Size : 47,8 Mb
Release : 2019-11-28
Category : Computers
ISBN : 9789811215643
The Pacific Symposium on Biocomputing (PSB) 2020 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2020 will be held on January 3 -7, 2020 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2020 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
