Deep Learning For Targeted Treatments
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Deep Learning For Targeted Treatments
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Author : Rishabha Malviya
language : en
Publisher: John Wiley & Sons
Release Date : 2022-09-20
Deep Learning For Targeted Treatments written by Rishabha Malviya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Computers categories.
DEEP LEARNING FOR TREATMENTS The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.
Deep Learning In Biology And Medicine
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Author : Davide Bacciu
language : en
Publisher: World Scientific
Release Date : 2022-01-17
Deep Learning In Biology And Medicine written by Davide Bacciu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-17 with Computers categories.
Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.
Deep Learning For Targeted Treatments
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Author : Rishabha Malviya
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-11
Deep Learning For Targeted Treatments written by Rishabha Malviya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-11 with Computers categories.
DEEP LEARNING FOR TREATMENTS The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.
Artificial Intelligence In Oncology Drug Discovery And Development
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Author : John Cassidy
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-09-09
Artificial Intelligence In Oncology Drug Discovery And Development written by John Cassidy and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-09 with Medical categories.
There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence.
Deep Learning For Medical Decision Support Systems
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Author : Utku Kose
language : en
Publisher: Springer Nature
Release Date : 2020-06-17
Deep Learning For Medical Decision Support Systems written by Utku Kose and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-17 with Technology & Engineering categories.
This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.
Machine And Deep Learning In Oncology Medical Physics And Radiology
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Author : Issam El Naqa
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Machine And Deep Learning In Oncology Medical Physics And Radiology written by Issam El Naqa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Science categories.
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Artificial Intelligence Platform For Molecular Targeted Therapy A Translational Science Approach
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Author : Ariel Fernandez
language : en
Publisher: World Scientific
Release Date : 2021-03-12
Artificial Intelligence Platform For Molecular Targeted Therapy A Translational Science Approach written by Ariel Fernandez and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-12 with Science categories.
In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.
Deep Learning For Personalized Healthcare Services
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Author : Vishal Jain
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2021-10-25
Deep Learning For Personalized Healthcare Services written by Vishal Jain and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-25 with Computers categories.
THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Novel Radiomics And Deep Learning Approaches Targeting The Tumor Environment To Predict Response To Chemotherapy
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Author : Nathaniel Braman
language : en
Publisher:
Release Date : 2020
Novel Radiomics And Deep Learning Approaches Targeting The Tumor Environment To Predict Response To Chemotherapy written by Nathaniel Braman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Biochemical markers categories.
As the arsenal of therapeutic strategies in the fight against cancer grows, so too does the need for predictive biomarkers that can precisely guide their use in order to match patients with their optimal personalized treatment plan. Currently, clinicians often have little recourse but to initiate treatment and monitor a tumor for signs of response or progression, which exposes non-responsive patients to overtreatment, harmful side effects, and windows of ineffective therapy that increase a patient's risk of progression or metastasis. Thus, there is an urgent need for new sources of predictive biomarkers to help more effectively plan personalized treatment strategies. Radiological images acquired before treatment may contain previously untapped predictive information that can be quantified in the form of computational imaging biomarkers. The vast majority of existing computational imaging biomarkers provides analysis limited to the tumor region itself. However, the tumor environment contains critical biological information pertinent to tumor progression and treatment outcome, such as tumor-associated vascularization and immune response. This dissertation focuses on the development of new, biologically-inspired computational imaging biomarkers targeting the tumor environment for the prediction of response to a wide range of chemotherapeutic and targeted treatment strategies in oncology. First, we explore measurements of textural heterogeneity within the tumor and surrounding peritumoral environment, and demonstrate the ability to predict therapeutic response and tumor biology to neoadjuvant chemotherapy in primary and targeted therapy in primary and metastatic breast cancer. Second, we introduce morphologic techniques for the quantification of the twistedness and organization of the tumor-associated vasculature, and demonstrate their association with response and survival following four different therapeutic strategies in breast cancer MRI and non-small cell lung cancer CT. Third, we present novel deep learning strategies involving the training of specialized convolutional neural networks tailored to the prediction of response to chemotherapy and targeted therapy from MRI. Finally, we present a novel methodology to combine the computational imaging biomarkers across both the tumor and peritumoral environment.
Advanced Machine Learning Approaches In Cancer Prognosis
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Author : Janmenjoy Nayak
language : en
Publisher: Springer Nature
Release Date : 2021-05-29
Advanced Machine Learning Approaches In Cancer Prognosis written by Janmenjoy Nayak and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-29 with Technology & Engineering categories.
This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.