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Author: Chengwei Lei Publisher: ISBN: 9781321194784 Category : Languages : en Pages : 84
Book Description
Recent advances in high-throughput technology have dramatically increased the amount of available experimental data in biological research, such as complete genome sequences, transcriptomic data under diverse conditions, and interaction networks among different components in the cell. However, the exponentially increasing data challenges the conventional gene-based paradigm to understand biology. Efficient and effective computational methods are needed to clean, analyze and model the data from a whole systems perspective. To achieve these goals, this research attempts to addresses several key challenging problems in bioinformatics that are associated with constructing functional gene networks and utilizing the networks for better understanding and prediction of cancer development and progression. Specifically, this dissertation has made significant contributions in three relatively independent but highly related sub-areas of bioinformatics. First, an optimization algorithm based on particle swarm intelligence has been developed to efficiently identify transcription factor binding sites (TFBS) motifs that often consist of two short DNA sequence patterns separated by a variable length gap. This work can help decipher the complex gene regulatory networks and understand gene functions. Second, a novel random walk based algorithm has been proposed to remove spurious protein-protein interactions and predict new interactions based solely on the basis of the topological properties of proteins in an existing protein-protein interaction network. Experimental results showed that the method can significantly improve the quality of existing protein-protein interaction networks in yeast and human, which in turn resulted in much better accuracy of protein complex prediction. Finally, new method has been developed to improve cancer prognosis by combining gene expression microarray data and protein-protein interaction networks. Utilizing a random walk algorithm, our method was able to identify novel biomarker genes that can significantly improve the prognosis accuracy of breast cancer metastasis. Importantly, these individual biomarkers are not differentially expressed and therefore would not be detectable by conventionally classification methods that treat individual genes as independent features. Taken together, the results achieved in these diverse sub-areas demonstrated the feasibility of using machine learning approaches to assist biological research at a systems level.
Author: Chengwei Lei Publisher: ISBN: 9781321194784 Category : Languages : en Pages : 84
Book Description
Recent advances in high-throughput technology have dramatically increased the amount of available experimental data in biological research, such as complete genome sequences, transcriptomic data under diverse conditions, and interaction networks among different components in the cell. However, the exponentially increasing data challenges the conventional gene-based paradigm to understand biology. Efficient and effective computational methods are needed to clean, analyze and model the data from a whole systems perspective. To achieve these goals, this research attempts to addresses several key challenging problems in bioinformatics that are associated with constructing functional gene networks and utilizing the networks for better understanding and prediction of cancer development and progression. Specifically, this dissertation has made significant contributions in three relatively independent but highly related sub-areas of bioinformatics. First, an optimization algorithm based on particle swarm intelligence has been developed to efficiently identify transcription factor binding sites (TFBS) motifs that often consist of two short DNA sequence patterns separated by a variable length gap. This work can help decipher the complex gene regulatory networks and understand gene functions. Second, a novel random walk based algorithm has been proposed to remove spurious protein-protein interactions and predict new interactions based solely on the basis of the topological properties of proteins in an existing protein-protein interaction network. Experimental results showed that the method can significantly improve the quality of existing protein-protein interaction networks in yeast and human, which in turn resulted in much better accuracy of protein complex prediction. Finally, new method has been developed to improve cancer prognosis by combining gene expression microarray data and protein-protein interaction networks. Utilizing a random walk algorithm, our method was able to identify novel biomarker genes that can significantly improve the prognosis accuracy of breast cancer metastasis. Importantly, these individual biomarkers are not differentially expressed and therefore would not be detectable by conventionally classification methods that treat individual genes as independent features. Taken together, the results achieved in these diverse sub-areas demonstrated the feasibility of using machine learning approaches to assist biological research at a systems level.
Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
Book Description
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: Faheem Masoodi Publisher: CRC Press ISBN: 1000833798 Category : Computers Languages : en Pages : 233
Book Description
The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms. Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment. Highlights include: Artificial Intelligence in treating and diagnosing schizophrenia An analysis of ML’s and DL’s financial effect on healthcare An XGBoost-based classification method for breast cancer classification Using ML to predict squamous diseases ML and DL applications in genomics and proteomics Applying ML and DL to biological data
Author: Sujata Dash Publisher: CRC Press ISBN: 1000534006 Category : Computers Languages : en Pages : 382
Book Description
Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Author: Charu C. Aggarwal Publisher: CRC Press ISBN: 1498760589 Category : Business & Economics Languages : en Pages : 710
Book Description
Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi
Author: Sujata Dash Publisher: John Wiley & Sons ISBN: 111971124X Category : Computers Languages : en Pages : 450
Book Description
BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
Author: Russ B Altman Publisher: World Scientific ISBN: 9814644749 Category : Science Languages : en Pages : 516
Book Description
The Pacific Symposium on Biocomputing (PSB) 2015 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 2015 will be held from January 4 - 8, 2015 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2015 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.
Author: H.D.Cheng Publisher: Infinite Study ISBN: Category : Languages : en Pages : 19
Book Description
Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast.
Author: Utku Kose Publisher: CRC Press ISBN: 1000406423 Category : Technology & Engineering Languages : en Pages : 365
Book Description
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.
Author: S. Kevin Zhou Publisher: Academic Press ISBN: 0323858880 Category : Computers Languages : en Pages : 544
Book Description
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Author: Chengwei Lei
Author: Chengwei Lei
Author: William L. William L. Hamilton
Author: Faheem Masoodi
Author: Sujata Dash
Author: Charu C. Aggarwal
Author: Sujata Dash
Author: Russ B Altman
Author: H.D.Cheng
Author: Utku Kose
Author: S. Kevin Zhou