Download or read book Machine Learning and Network Driven Integrative Genomics written by Mehdi Pirooznia and published by Frontiers Media SA. This book was released on 2021-04-29 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Machine Learning Applications for Genomics written by Sanjiban Sekhar Roy and published by Springer Nature. This book was released on 2022-06-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence written by and published by BoD – Books on Demand. This book was released on 2019-07-31 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.
Download or read book Big Data Analytics in Genomics written by Ka-Chun Wong and published by Springer. This book was released on 2016-10-24 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Integrative Genomics and Network Biology in Livestock and other Domestic Animals written by David E. MacHugh and published by Frontiers Media SA. This book was released on 2020-09-11 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Future of AI in Biomedicine and Biotechnology written by Khade, Shankar Mukundrao and published by IGI Global. This book was released on 2024-05-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry is grappling with numerous challenges, including rising costs, inefficiencies in service delivery, and the need for personalized treatment approaches. Traditional healthcare management and delivery methods must be improved in addressing these issues, leading to a growing demand for innovative solutions. Additionally, the exponential growth of medical data and the complexity of biomedical research and biotechnology presents a daunting challenge in harnessing this data effectively for improved patient care and medical advancements. There is a pressing need for a comprehensive understanding of how artificial intelligence (AI) can be leveraged to tackle these challenges and drive meaningful change in the healthcare sector. Future of AI in Biomedicine and Biotechnology offers a timely and insightful solution to the challenges faced by the healthcare industry. This book is not just a theoretical exploration; it is a practical roadmap for healthcare professionals, researchers, policymakers, and entrepreneurs seeking to navigate the complexities of AI in healthcare. By exploring the intersection of AI with biomedical sciences and biotechnology, this book provides a comprehensive guide to harnessing the power of AI for transformative healthcare innovation.
Download or read book Machine Learning Applications in Integrative Cancer Genomics written by and published by . This book was released on 2015 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Driven Science for Clinically Actionable Knowledge in Diseases written by Daniel Catchpoole and published by CRC Press. This book was released on 2023-12-06 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods, meaningful visualisation and human-information interaction. This edited volume covers state-of-the-art theory, method, models, design, evaluation and applications in computational and visual analytics in desktop, mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis, including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments. By studying how data and visual analytics have been implemented into the healthcare domain, the book demonstrates how analytics influences the domain through improving decision making, specifying diagnostics, selecting the best treatments and generating clinical certainty.
Download or read book Machine Learning in Dentistry written by Ching-Chang Ko and published by Springer Nature. This book was released on 2021-07-24 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Issues in Artificial Intelligence Robotics and Machine Learning 2013 Edition written by and published by ScholarlyEditions. This book was released on 2013-05-01 with total page 1211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Expert Systems. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Expert Systems in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Download or read book Knowledge and Systems Engineering written by Viet-Ha Nguyen and published by Springer. This book was released on 2014-09-29 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains papers presented at the Sixth International Conference on Knowledge and Systems Engineering (KSE 2014), which was held in Hanoi, Vietnam, during 9–11 October, 2014. The conference was organized by the University of Engineering and Technology, Vietnam National University, Hanoi. Besides the main track of contributed papers, this proceedings feature the results of four special sessions focusing on specific topics of interest and three invited keynote speeches. The book gathers a total of 51 carefully reviewed papers describing recent advances and development on various topics including knowledge discovery and data mining, natural language processing, expert systems, intelligent decision making, computational biology, computational modeling, optimization algorithms, and industrial applications.
Download or read book Towards Integrative Machine Learning and Knowledge Extraction written by Andreas Holzinger and published by Springer. This book was released on 2017-10-27 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: The BIRS Workshop “Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets” (15w2181), held in July 2015 in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of “hot topics” toward tackling the grand challenge of reaching a level of useful and useable computational intelligence with a focus on real-world problems, such as in the health domain. This encompasses learning from prior data, extracting and discovering knowledge, generalizing the results, fighting the curse of dimensionality, and ultimately disentangling the underlying explanatory factors in complex data, i.e., to make sense of data within the context of the application domain. The workshop aimed to contribute advancements in promising novel areas such as at the intersection of machine learning and topological data analysis. History has shown that most often the overlapping areas at intersections of seemingly disparate fields are key for the stimulation of new insights and further advances. This is particularly true for the extremely broad field of machine learning.
Download or read book Integrative Machine Learning and Network Mining Models for the Inference of Regulatory Elements and Interactions in Human Cells written by Asa Thibodeau and published by . This book was released on 2018 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increase in diverse genome profiling technologies and publicly available ontology databases ranging from open chromatin profiles to the 3D structure of the genome, it is imperative to build novel computational methods that take full advantage of these diverse datasets to uncover the regulatory mechanisms behind cellular functions. Integrating these datasets offers the opportunity to identify regulatory elements (id est, promoter, enhancers, et cetera) and interactions critical for cell-type-specific functions. Here, the goal's two fold: 1) inference of regulatory interactions and networks from 3D chromatin interaction datasets and 2) inference of cell-specific and non-specific regulatory elements such as enhancers (regulatory elements that target gene promoters and regulate their expression). To address the first goal, two software tools were developed: (1) a web-accessible application: Querying and visualizing chromatin Interaction Network (QuIN) and (2) a pathway analysis prioritization tool: Triangulation of Perturbation Origins and Identification of Non-Coding Targets (TriPOINT). QuIN enables users to easily mine chromatin interaction datasets and integrate them with other sources such as SNPs and epigenetic marks to ultimately build networks to query and visualize them in downstream analyses and to prioritize genomic loci (id est, disease-causing variants). Similarly, TriPOINT uses pathways in conjunction with chromatin interaction networks to identify perturbed genes in treatment vs. control cases, implementing pathway topology based approaches for identifying inconsistencies in pathways and incorporating the capabilities of QuIN to integrate non-coding regulators targeting genes in these pathways through chromatin interaction data. The second goal was achieved using two approaches. First, features obtained from network mining were trained on support vector machines to assess the predictive power in identifying cell-type-specific promoters (broad domains) and enhancers (super enhancers) from chromatin interaction networks. Network signatures were mined in three cell lines (MCF-7, K562, and GM12878) using QuIN across multiple chromatin interaction assays (ChIA-PET, Hi-C, and HiChIP) and it was discovered that network related features could effectively discriminate typical promoters and enhancers from cell-type-specific ones. Second, features from Assay for Transposase Accessible Chromatin (ATAC-seq) were profiled to identify enhancers from accessible chromatin in neural network models. Models were highly predictive of enhancers; useful for individual specific and clinical sample settings.
Download or read book Systems Medicine written by and published by Academic Press. This book was released on 2020-08-24 with total page 1571 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technological advances in generated molecular and cell biological data are transforming biomedical research. Sequencing, multi-omics and imaging technologies are likely to have deep impact on the future of medical practice. In parallel to technological developments, methodologies to gather, integrate, visualize and analyze heterogeneous and large-scale data sets are needed to develop new approaches for diagnosis, prognosis and therapy. Systems Medicine: Integrative, Qualitative and Computational Approaches is an innovative, interdisciplinary and integrative approach that extends the concept of systems biology and the unprecedented insights that computational methods and mathematical modeling offer of the interactions and network behavior of complex biological systems, to novel clinically relevant applications for the design of more successful prognostic, diagnostic and therapeutic approaches. This 3 volume work features 132 entries from renowned experts in the fields and covers the tools, methods, algorithms and data analysis workflows used for integrating and analyzing multi-dimensional data routinely generated in clinical settings with the aim of providing medical practitioners with robust clinical decision support systems. Importantly the work delves into the applications of systems medicine in areas such as tumor systems biology, metabolic and cardiovascular diseases as well as immunology and infectious diseases amongst others. This is a fundamental resource for biomedical students and researchers as well as medical practitioners who need to need to adopt advances in computational tools and methods into the clinical practice. Encyclopedic coverage: ‘one-stop’ resource for access to information written by world-leading scholars in the field of Systems Biology and Systems Medicine, with easy cross-referencing of related articles to promote understanding and further research Authoritative: the whole work is authored and edited by recognized experts in the field, with a range of different expertise, ensuring a high quality standard Digitally innovative: Hyperlinked references and further readings, cross-references and diagrams/images will allow readers to easily navigate a wealth of information
Download or read book Deep Learning for Biomedical Applications written by Utku Kose and published by CRC Press. This book was released on 2021-07-20 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.
Download or read book Data Mining and Medical Knowledge Management Cases and Applications written by Berka, Petr and published by IGI Global. This book was released on 2009-02-28 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.
Download or read book CODE BLUE TO CODE AI written by SUDHANSHU TONPE and published by Shashwat Publication. This book was released on 2024-08-23 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: The unique selling proposition (USP) of "Code Blue to Code AI" lies in its comprehensive exploration of the transformative impact of artificial intelligence (AI) on the healthcare industry. Authored by Dr. Sudhanshu Tonpe, the book stands out by: Expertise: Dr. Tonpe, an accomplished radiologist, brings his firsthand experience and insights to provide an authoritative perspective on the integration of AI in healthcare. Holistic Coverage: The book covers various facets, including medical diagnostics, drug discovery, patient engagement, and the collaboration between AI and healthcare professionals, offering a well-rounded understanding of the subject. Real-world Examples: By incorporating real-world case studies and examples, the book bridges the gap between theory and practical application, making the content relatable and insightful. Accessible Language: Dr. Tonpe communicates complex concepts in a clear and accessible language, making the book suitable for both healthcare professionals and a broader audience interested in the intersection of medicine and AI. Current Relevance: Given the dynamic nature of healthcare and AI, the book is likely to address contemporary issues and trends, keeping the content relevant and up-to-date. In essence, "Code Blue to Code AI" offers a unique blend of expertise, comprehensive coverage, practical examples, and accessibility, making it a valuable resource for anyone interested in the future of healthcare through the lens of artificial intelligence.
