Application of Novel Statistical and Machine-learning Methods to High-dimensional Clinical Cancer and (Multi-)Omics data PDF Download
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Author: Chao Xu
Publisher: Frontiers Media SA
ISBN: 2889714365
Category : Science
Languages : en
Pages : 136
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Book Description
Author: Chao Xu
Publisher: Frontiers Media SA
ISBN: 2889714365
Category : Science
Languages : en
Pages : 136
Get Book Here
Book Description
Author: Xiaochun Li
Publisher: Springer Science & Business Media
ISBN: 0387697659
Category : Medical
Languages : en
Pages : 164
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Book Description
Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.
Author: Angelo Facchiano
Publisher: Frontiers Media SA
ISBN: 2889637522
Category :
Languages : en
Pages : 175
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Book Description
Author: De-Shuang Huang
Publisher: Springer Nature
ISBN: 9819947499
Category : Technology & Engineering
Languages : en
Pages : 835
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Book Description
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:
Publisher:
ISBN:
Category :
Languages : en
Pages : 118
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Book Description
Advancing technology has enabled us to study the molecular configuration of single cells or whole tissue samples. Molecular biology produces vast amounts of high-dimensional omics data at continually decreasing costs, so that molecular screens are increasingly often used in clinical applications. Personalized diagnosis or prediction of clinical treatment outcome based on high-throughput omics data are modern applications of machine learning techniques to clinical problems. In practice, clinical parameters, such as patient health status or toxic reaction to therapy, are often measured on an ...
Author: Abedalrhman Alkhateeb
Publisher: Springer
ISBN: 9783031365010
Category : Science
Languages : en
Pages : 0
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Book Description
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.
Author: Ching-Chang Ko
Publisher: Springer Nature
ISBN: 3030718816
Category : Medical
Languages : en
Pages : 186
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Book Description
This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.
Author: Hongda Liu
Publisher: Frontiers Media SA
ISBN: 2832554741
Category : Medical
Languages : en
Pages : 535
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Book Description
More and more medical centers are now combining high-resolution CT scans well with deep learning and artificial intelligence for lung cancer screening, resulting in significantly improved diagnostic sensitivity. Furthermore, the increased molecular alterations in lung cancer were demonstrated not only in tumor tissue, but also in other body organs. For example, circulating tumor DNA combined with next-generation sequencing is now becoming a popular method for lung cancer diagnosis and therapeutic monitoring. Therefore, the first focus of this topic is on such achievements in early diagnosis of lung cancer, especially non-invasive tests such as liquid biopsy.
Author: Kang Ning
Publisher: Springer Nature
ISBN: 9811982104
Category : Medical
Languages : en
Pages : 173
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Book Description
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: George Tseng
Publisher: Cambridge University Press
ISBN: 1316299406
Category : Medical
Languages : en
Pages : 497
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Book Description
In most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. As the technologies have become mature and the price affordable, omics data are rapidly generated, and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field. This book provides comprehensive coverage of these topics and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.
