By Andres Kriete, Roland Eils
Structures Biology is worried with the quantitative examine of complicated biosystems on the molecular, mobile, tissue, and platforms scales. Its concentration is at the functionality of the process as a complete, instead of on person components. This fascinating new enviornment applies mathematical modeling and engineering the way to the learn of organic structures. This publication is the 1st of its type to target the newly rising box of platforms biology with an emphasis on computational ways. The paintings covers new innovations, tools for info garage, mining and information extraction, opposite engineering of gene and metabolic networks, in addition to modelling and simulation of multi-cellular structures. significant topics comprise innovations for predicting organic houses and strategies for elucidating structure-function relationships.
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E-Cell 2: Multi-platform E-Cell simulation system. Bioinformatics 19(13):1727–1729. , and Wodak, S. (2000). Representing and analyzing molecular and cellular function using the computer. Biol. Chem. 381(9/10):921–935. , and Wagner, L. (2003). Database resources of the National Center for Biotechnology. Nucleic Acids Res. 31(1):28–33. , and Urbach, S. (2001). The TRANSFAC system on gene expression regulation. Nucleic Acids Res. 29(1):281–283. , Yost, K. III, Yates, J. , and Waters, M. (2004). CEBS object model for systems biology data, SysBio-OM.
We developed (Hu et al. 2004) a robust system, GE-Miner (Gene Expression Miner), to integrate cluster ensemble and text mining to overcome this limitation. Here we further enhance GE-Miner by integrating biomedical ontology. Our objective is not to address all research issues raised in biomedical text clustering and summarization. As a complement to a number of relevant techniques—such as text mining, automatic text classification and others—we focus on exploiting our capability to automatically extract and analyze knowledge from text documents through text clustering and summarization.
2) Here, p(xi) denotes the appearance probability of word xi and p(xi, yj) denotes the probability that xi and yj appear at the same position in two text segments. Following the same method proposed in Li et al. 3b with prealigned training data. 3b) allpairs x , y ) Here, C(xi) denotes the count of word xi appearing in the training corpus, and C(xi, yj ) denotes the number of aligned pairs (xi, yj) being observed in the training set. 1 recursively. The scores are stored in a matrix as M = M(xi, yj).
Computational Systems Biology by Andres Kriete, Roland Eils