By Andres Kriete, Roland Eils
ISBN-10: 012088786X
ISBN-13: 9780120887866
Platforms Biology is worried with the quantitative learn of advanced biosystems on the molecular, mobile, tissue, and platforms scales. Its concentration is at the functionality of the procedure as a complete, instead of on person components. This intriguing new enviornment applies mathematical modeling and engineering how you can the examine of organic structures. This publication is the 1st of its style to target the newly rising box of platforms biology with an emphasis on computational methods. The paintings covers new suggestions, tools for info garage, mining and data extraction, opposite engineering of gene and metabolic networks, in addition to modelling and simulation of multi-cellular structures. critical subject matters comprise ideas for predicting organic houses and techniques for elucidating structure-function relationships.
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Extra resources for Computational Systems Biology
Example text
S21 S22 ... Doc Cluster k Mutual Reinforcement based Keyterm and Sentence Extraction TOP m sentences ... Ontologyenriched vector representation Clustering Original Document Set Doc Cluster 1 TOP n terms Kk1 Kk2 ... Sk1 Sk2 ... 2. Data flow of text clustering and text summarization. Final Summary Xiaohua Hu 47 ciency). Ontology-based summaries can actually capture the essential information of textual documents at different levels of granularity. This issue is closely related to the compression rate and coverage/diversity of summaries (Hahn and Mani 2000).
One interesting, yet largely unexplored, aspect of IE is that potential relationships usually occur redundantly in text files. In deciding which putative relationships should be extracted, a key problem is how to combine evidences across the multiple occurrences of these relationships. We present a statistical method for addressing it. For each relationship Tj, we store the set of patterns P = {Pi} (i = 1, . . , m) that produce it, together with the measure of similarity match Match(Tj, Pi) between the context in which the relationship occurred and the matching pattern Pi.
It is very important to develop efficient and effective technologies that automatically search large collections of biomedical literature, extract and mine the important biological relationships such as protein-protein interaction, functionalities of the genes, etc, so that domain experts can analyze this information to form new hypotheses, conduct new experiments and enable new discoveries in systems biology research. In this chapter, we discuss some of the latest natural language processing and data mining techniques in this area and demonstrate their usefulness in chromatin protein interaction study and microarray data analysis.
Computational Systems Biology by Andres Kriete, Roland Eils
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