COMPUTATIONAL BIOLOGY and High-Performance Computing.
The article describes the role of high-performance computing (HPC) in solving challenges and problems in biology. Computer scientists and biomedical researchers face the challenge of transforming data into models and simulations that will enable scientists for the first time to gain a profound under...
| Publicado en: | Communications of the ACM Vol. 47; no. 11; pp. 34 - 42 |
|---|---|
| Autor principal: | |
| Formato: | Artículo |
| Publicado: |
Association for Computing Machinery
Nov2004
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=14957998&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 14957998 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2004 vid: 47 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 14957998 10.1145/1029496.1029523 ppf: 34 ppct: 8 formats: tig: atl: COMPUTATIONAL BIOLOGY and High-Performance Computing. aug: au: Bader, David A. affil: Associate Professor, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque. Lecturer, Department of Computer Science, University of New Mexico, Albuquerque. su: High performance computing Computational biology Biomolecules Protein folding Computer scientists Electronic data processing sug: subj: High performance computing Computational biology Biomolecules Protein folding Computer scientists Electronic data processing ab: The article describes the role of high-performance computing (HPC) in solving challenges and problems in biology. Computer scientists and biomedical researchers face the challenge of transforming data into models and simulations that will enable scientists for the first time to gain a profound understanding of the deepest biological functions. Solving biological problems may require HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms but have massive computational requirements due to the large data sets that must be analyzed. Understanding evolution and the basic structure and function of proteins are two grand challenge problems in biology that can be solved only through the use of high-performance computing. Thus, the article investigates problems requiring massive parallelism due to inherent algorithmic complexity such as protein folding or due to being NP-hard such as inferring evolutionary histories from genetic information. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2004 holdings: @attributes: islocal: N |
|---|