Knowledge Annotation Making Implicit Knowledge Explicit
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Knowledge Annotation: Making Implicit Knowledge Explicit
Author: Alexiei Dingli
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-04-06
Did you ever read something on a book, felt the need to comment, took up a pencil and scribbled something on the books’ text’? If you did, you just annotated a book. But that process has now become something fundamental and revolutionary in these days of computing. Annotation is all about adding further information to text, pictures, movies and even to physical objects. In practice, anything which can be identified either virtually or physically can be annotated. In this book, we will delve into what makes annotations, and analyse their significance for the future evolutions of the web. We will explain why it was thought to be unreasonable to annotate documents manually and how Web 2.0 is making us rethink our beliefs. We will have a look at tools which make use of Artificial Intelligence techniques to support people in the annotation task. Behind these tools, there exists an important property of the web known as redundancy; we will explain what it is and show how it can be exploited. Finally we will gaze into the crystal ball and see what we might expect to see in the future. Until people understand what the web is all about and its grounding in annotation, people cannot start appreciating it. And until they do so, they cannot start creating the web of the future.
Ai And Big Data Sciences For Bioinformatics And Systems Medicine
This book aims to provide a comprehensive overview of how artificial intelligence (AI) and big data analytics are transforming bioinformatics and biomedical research. It covers a wide range of computational methods applied to biomedical big data, including genomics, transcriptomics, proteomics, and imaging data, with a focus on their role in precision medicine.The book is structured to guide readers from foundational AI models and data integration techniques to advanced applications in systems biology and drug discovery. It explores key topics such as single-cell and spatial omics analysis, genetic variation studies, and computational modeling of disease processes. Additionally, it highlights cutting-edge approaches in synthetic biology, mRNA optimization, and small molecule generation.Designed for researchers, bioinformaticians, and clinicians, this book bridges the gap between computational sciences and biomedical applications. It aims to equip readers with the necessary knowledge to develop AI-driven solutions for diagnosing diseases, predicting treatment responses, and designing novel therapeutics. By integrating theoretical insights with real-world applications, the book serves as both an educational resource and a reference for ongoing advancements in AI-driven biomedical research.
From Curve Fitting to Machine Learning
Author: Achim Zielesny
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-07-28
The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. These sections may be skipped without affecting the main road but they will open up possibly interesting insights beyond the mere data massage. All topics are completely demonstrated with the aid of the commercial computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source so the detailed code of every method is freely accessible. All examples and applications shown throughout the book may be used and customized by the reader without any restrictions. The target readerships are students of(computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction to these topics. Readers with programming skills may easily port and customize the provided code.