Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods
Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods

Author: Lilhore, Umesh Kumar

Publisher: IGI Global

Published: 2024-03-22

Total Pages: 418

ISBN-13:

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Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.

Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods
Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods

Author: Vishal Dutt

Publisher:

Published: 2024

Total Pages: 0

ISBN-13:

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Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics . This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences. Designed for academic scholars and practitioners, as well as upper-level undergraduates and graduates seeking to expand their knowledge, this book is a must-read for anyone passionate about the intersection of data science and human biology. Healthcare professionals, biotechnologists, and academics alike will find this resource invaluable for advancing their understanding and capabilities in the dynamic field of bioinformatics.

Ethnobotanical Insights Into Medicinal Plants
Ethnobotanical Insights Into Medicinal Plants

Author: Musaddiq, Sara

Publisher: IGI Global

Published: 2024-05-07

Total Pages: 406

ISBN-13:

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A significant gap exists between traditional knowledge and modern scientific understanding of phytochemicals and ethnobotanical wisdom in botanical science. Despite the commonplace culinary use of many herbs and seasonings, their historical, botanical, and medicinal dimensions often remain overlooked. This gap hinders advancements in various disciplines, including chemistry, pharmacology, botany, and agriculture, limiting the potential for innovative research and sustainable solutions. Ethnobotanical Insights into Medicinal Plants bridges this gap by comprehensively examining these plants' morphology, cultivation techniques, and classifications. This book illuminates their untapped potential and catalyzes innovative healthcare, agriculture, and manufacturing research. Integrating ethnobotanical observations with scientific progress enhances the intellectual domain for academics, researchers, and professionals, paving the way for environmentally sustainable methods of producing bioactive substances.

Applications of Parallel Data Processing for Biomedical Imaging
Applications of Parallel Data Processing for Biomedical Imaging

Author: Khan, Rijwan

Publisher: IGI Global

Published: 2024-04-26

Total Pages: 367

ISBN-13:

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Despite the remarkable progress witnessed in the last decade in big data utilization and parallel processing techniques, a persistent disparity exists between the capabilities of computer-aided diagnosis systems and the intricacies of practical healthcare scenarios. This disconnection is particularly evident in the complex landscape of artificial intelligence (AI) and IoT innovations within the biomedical realm. The need to bridge this gap and explore the untapped potential in healthcare and biomedical applications has never been more crucial. As we navigate through these challenges, Applications of Parallel Data Processing for Biomedical Imaging offers insights and solutions to reshape the future of biomedical research. The objective of Applications of Parallel Data Processing for Biomedical Imaging is to bring together researchers from both the computer science and biomedical research communities. By showcasing state-of-the-art deep learning and large data analysis technologies, the book provides a platform for the cross-pollination of ideas between AI-based and traditional methodologies. The collaborative effort seeks to have a substantial impact on data mining, AI, computer vision, biomedical research, healthcare engineering, and other related fields. This interdisciplinary approach positions the book as a cornerstone for scholars, professors, and professionals working in software and medical fields, catering to both graduate and undergraduate students eager to explore the evolving landscape of parallel computing, artificial intelligence, and their applications in biomedical research.

Reshaping Healthcare with Cutting-Edge Biomedical Advancements
Reshaping Healthcare with Cutting-Edge Biomedical Advancements

Author: Prabhakar, Pranav Kumar

Publisher: IGI Global

Published: 2024-05-06

Total Pages: 504

ISBN-13:

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Despite remarkable advancements in biomedical research, the healthcare industry faces challenges in effectively translating these discoveries into tangible patient benefits. Healthcare professionals often need help to keep pace with the rapid evolution of medical knowledge, leading to variations in patient care and treatment outcomes. Policymakers and educators may need more insight to leverage recent biomedical developments in shaping effective health policies and educational curricula. Additionally, ethical considerations surrounding emerging technologies like gene editing and Artificial Intelligence (AI) in healthcare pose complex dilemmas that require careful navigation. Reshaping Healthcare with Cutting-Edge Biomedical Advancements offers a comprehensive solution to these challenges. By providing a detailed exploration of the latest breakthroughs in genomics, regenerative therapies, neurobiology, AI, and more, this book equips healthcare professionals with the knowledge needed to make informed decisions about patient care. It also guides policymakers and educators, offering insights into the implications of recent biomedical advancements for shaping health policies and educational programs.

Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology
Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology

Author: Arora, Geeta

Publisher: IGI Global

Published: 2024-07-18

Total Pages: 356

ISBN-13:

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In today's data-driven world, understanding and interpreting statistical information is more critical than ever, especially in medicine, where statistical methods are used to design and analyze clinical trials, study the distribution of disease in populations, and develop new treatments. In the era of evidence-based medicine, Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology addresses the critical need for a grasp of statistical concepts. This book delves into biostatistics, clinical trials, and epidemiology, offering a robust foundation for understanding and interpreting statistical information in medicine. It explores biostatistics, elucidating fundamental elements such as probability, sampling, and hypothesis testing. The section on clinical trials covers the entire spectrum from trial design to ethical considerations, providing an invaluable resource for researchers navigating the complexities of medical research. Epidemiology, a cornerstone of public health, is examined in the book, offering insights into the distribution and determinants of diseases in populations. The application-focused section further extends the utility of medical statistics, encompassing public health, healthcare policy, and drug development.

Interpretable Machine Learning
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Unsupervised Learning
Unsupervised Learning

Author: Geoffrey Hinton

Publisher: MIT Press

Published: 1999-05-24

Total Pages: 420

ISBN-13: 9780262581684

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Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher: Springer

Published: 2016-08-23

Total Pages: 0

ISBN-13: 9781493938438

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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

SIGIR ’94
SIGIR ’94

Author: W. Bruce Croft

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 371

ISBN-13: 144712099X

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Information retrieval (IR) is becoming an increasingly important area as scientific, business and government organisations take up the notion of "information superhighways" and make available their full text databases for searching. Containing a selection of 35 papers taken from the 17th Annual SIGIR Conference held in Dublin, Ireland in July 1994, the book addresses basic research and provides an evaluation of information retrieval techniques in applications. Topics covered include text categorisation, indexing, user modelling, IR theory and logic, natural language processing, statistical and probabilistic models of information retrieval systems, routing, passage retrieval, and implementation issues.