An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

Bioinformatics (Adaptive Computation and Machine Learning)

Computers & Technology

Baldi, Pierre

Søren Brunak

Computer Science

The MIT Press

Throughout history, we have selected and manipulated the genomes of plants, animals, and even ourselves. Until now, however, such control could be exerted only at the level of the entire organism. Scientific and technological advances now allow us to manipulate genomes directly at the level of single genes and their constituents, with a speed and precision that far exceed what natural evolution has been able to achieve over the past 3.5 billion years. These advances open new possibilities for medicine, biotechnology, and society as a whole. We already have in vitro fertilization and animal cloning; in the future human cloning and the exploitation of embryonic stem cells, among other capabilities, may be routine. At the same time, we are developing machines that will surpass the human brain in raw computing power and building an interconnected world of information-processing devices that makes science fiction pale in comparison. In this book Baldi explores what it is about these phenomena that makes us so uneasy—the shattering of the human self as we know it. Through evolution our brains have been wired to provide us with an inner sense of self, a feeling that each of us is a unique individual delimited by precise boundaries. We have also been wired to reproduce ourselves in a certain way. Baldi argues that this self-centered view of the world is scientifically wrong. Its past success lies in its being an adequate model during our evolutionary bootstrapping: a world without molecular biotechnology, human cloning, and the Internet. Eventually we must come to terms with the fact that genomes, computations, and mind are fluid, continuous entities, in both space and time. The boundary between the self and the world has begun to blur and ultimately may evaporate entirely. Baldi offers not predictions but an open-eyed exploration of our current state of knowledge and the possibilities that lie ahead.

The Shattered Self: The End of Natural Evolution

Science & Math

Biological Sciences

Bradford Books

A guide to machine learning approaches and their application to the analysis of biological data.
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models―and to automate the process as much as possible.
In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.
This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

Brunak, Søren

An exploration of the far-reaching scientific and social changes made possible by advances in molecular biotechnology.

MIT Press]

This book covers the most important aspects of the web using modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet and looks at new topics such as web graphics and highlights recent work from experts in the field of networks such as Kleinberg, Leskovec, Faloutsos and Newman. Provides new material on link analysis, LDA and non parametric Bayesian methods and features a new section on topic modelling applied to large-scale software data.

Modeling the Internet and the Web: Probabilistic Methods and Algorithms (Wiley Series on Methods and Applications in Data Mining)

Frasconi, Paolo

Smyth, Padhraic

Networking & Cloud Computing

wiley

Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet. 
 Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level. Takes a modern approach based on mathematical, probabilistic, and graphical modeling. Provides an integrated presentation of theory, examples, exercises and applications. Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences. "This book is fascinating!" - David Hand (Imperial College, UK) "This book provides an extremely useful introduction to the intellectually stimulating problems of data mining electronic business." - Andreas S. Weigend (Chief Scientist, Amazon.com)

Modeling the Internet and the Web: Probabilistic Methods and Algorithms

Massive data acquisition technologies--such as genome sequencing, high-throughput drug screening, and DNA arrays--are in the process of revolutionizing biology and medicine. This concise, user-friendly and interdisciplinary guide to DNA microarray technology is an introduction and a reference for both biologists and computational scientists. The authors describe the underlying technologies and offer an awareness of the "noise" and pitfalls present in the data generated. They also provide an idea of the different data mining techniques and algorithms that are available to interpret data, and the advantages and disadvantages of each in differing situations.

DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling

Hatfield, G. Wesley

Cambridge University Press

Massive data acquisition technologies--such as genome sequencing, high-throughput drug screening, and DNA arrays--are in the process of revolutionizing biology and medicine. This concise, user-friendly and interdisciplinary guide to DNA microarray technology is an introduction and a reference for both biologists and computational scientists. The authors describe the underlying technologies and offer an awareness of the "noise" and pitfalls present in the data generated. They also provide an idea of the different data mining techniques and algorithms that are available to interpret data, and the advantages and disadvantages of each in differing situations.

This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.\nBook Description\nRigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.

Deep Learning in Science

See the best price to sell, buy, or rent books by Baldi, Pierre. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Baldi, Pierre

See the best price to sell, buy, or rent books by Baldi, Pierre. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Bioinformatics (Adaptive Computation and Machine Learning)

The Shattered Self: The End of Natural Evolution

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

The Shattered Self: The End of Natural Evolution

Modeling the Internet and the Web: Probabilistic Methods and Algorithms (Wiley Series on Methods and Applications in Data Mining)

Modeling the Internet and the Web: Probabilistic Methods and Algorithms

DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling

DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling

Deep Learning in Science