1,887 research outputs found
Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy
Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd
Maximizing Activity in Ising Networks via the TAP Approximation
A wide array of complex biological, social, and physical systems have
recently been shown to be quantitatively described by Ising models, which lie
at the intersection of statistical physics and machine learning. Here, we study
the fundamental question of how to optimize the state of a networked Ising
system given a budget of external influence. In the continuous setting where
one can tune the influence applied to each node, we propose a series of
approximate gradient ascent algorithms based on the Plefka expansion, which
generalizes the na\"{i}ve mean field and TAP approximations. In the discrete
setting where one chooses a small set of influential nodes, the problem is
equivalent to the famous influence maximization problem in social networks with
an additional stochastic noise term. In this case, we provide sufficient
conditions for when the objective is submodular, allowing a greedy algorithm to
achieve an approximation ratio of . Additionally, we compare the
Ising-based algorithms with traditional influence maximization algorithms,
demonstrating the practical importance of accurately modeling stochastic
fluctuations in the system
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