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Bayesian Networks in R: with Applications in Systems Biology (Use R!)

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While there have been significant advances in capturing data from the entities across complex real-world systems, their associations and relationships are largely unknown. Associations between the entities may reveal interesting system-level properties that may not be apparent otherwise. Often these associations are hypothesized by superimposing knowledge across distinct reductionist representations of these entities obtained from disparate sources. Such representations, while useful, may provide only an incomplete picture of the associations. This can be attributed to their dependence on prior knowledge and failure of the principle of superposition in general. Such representations may also be unhelpful in discovering novel undocumented associations. A more rigorous approach would be to identify associations from data measured simultaneously across the entities of interest from a given system. These data sets or digital signatures are quantized in time and amplitude and in turn may (dynamic) or may not (static) contain explicit temporal information. Symmetric measures such as correlation have been helpful in modeling direct associations as undirected graphs. However, it is well appreciated that the association between a given pair of entities may be indirect and often mediated through others.

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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Combinatorial Optimization: Theory and Algorithms (Algorithms and Combinatorics)
Combinatorial Optimization: Theory and Algorithms (Algorithms and Combinatorics)

This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It is based on numerous courses on combinatorial optimization and specialized topics, mostly at graduate level. This book reviews the...


Graphs, Networks and Algorithms (Algorithms and Computation in Mathematics)
Graphs, Networks and Algorithms (Algorithms and Computation in Mathematics)

This is the third edition of the classic textbook on the subject. With its clear writing, strong organization, and comprehensive coverage of essential theory it is like a personal guide through this important topic, and now has lots of extra material.

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Deep Learning Cookbook: Practical Recipes to Get Started Quickly
Deep Learning Cookbook: Practical Recipes to Get Started Quickly

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve...

Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural...

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