Download e-book for kindle: Computational Systems Biology by Andres Kriete, Roland Eils

By Andres Kriete, Roland Eils

ISBN-10: 012088786X

ISBN-13: 9780120887866

Platforms Biology is worried with the quantitative research of complicated 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 interesting new enviornment applies mathematical modeling and engineering how you can the examine of organic structures. This booklet is the 1st of its sort to target the newly rising box of structures biology with an emphasis on computational methods. The paintings covers new options, tools for info garage, mining and data extraction, opposite engineering of gene and metabolic networks, in addition to modelling and simulation of multi-cellular platforms. principal subject matters comprise techniques for predicting organic homes and techniques for elucidating structure-function relationships.

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Extra info for Computational Systems Biology

Example text

Strictly speaking, a frequent term set is not a cluster (candidate) but only the description of a cluster. The corresponding cluster itself consists of the set of documents containing all terms of the frequent terms.

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. Bioinformatics 20(13):2004–2015. Natural Language Processing and Ontology-enhanced Biomedical Literature Mining for Systems Biology Chapter 3 Xiaohua Hu College of Information Science and Technology, Drexel University, Philadelphia, Pennsylvania ABSTRACT Despite the rapid electronic dissemination of research results and experimental data in bioinformatics research, most biological knowledge and experimental results still exist only in text formats.

GEISHA (Blaschke et al. 2001) is based on a comparison of the frequency of abstracts linked to different gene clusters and contain- Xiaohua Hu 41 ing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other equally significant terms. Automatic text summarization has recently become an active research field related to many other research areas, such as IR (information retrieval), IE (information extraction), natural language processing, and machine learning (Goldstein et al.

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Computational Systems Biology by Andres Kriete, Roland Eils


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