20090219 The case for another systems biology modelling environment

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The document discusses the need for a new systems biology modelling environment. It provides context on systems biology and existing modelling approaches and software. It then makes the case that a new modelling environment could improve the user experience for biologists by making models easier to build and refine while allowing for more complex models at larger scales. Key details on existing challenges and the proposed new environment are outlined.

The case for another systems biology modelling environment Jonathan Blakes 19/2/2009 ASAP seminar

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Macrophage and Bacterium 2,000,000X 2002 Watercolor by David S. Goodsell

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-$K$ predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-$K$ retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach.

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“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological systems” (David Baltimore, Nobel Laureate)

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