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| Title: | Statistical and Mathematical Methods in Bioinformatics- An Overview |
| Authors: | Abbas, M
Mandal, S K
Srivastava, M |
| Issue Date: | 2006 |
| Publisher: | Allied Publishers Private Limited, New Delhi |
| Citation: | Statistical Advances in Biosciences & Bioinfomatics-2006-25-34 |
| Abstract: | Bioinformatics is the buzzword these days. Bioinformatics may be considered as
the study of information flow within biology and medicine. The first flow is the
flow of information from DNA code to biological function. Second flow is the
flow of 'in formation in the design and analysis of experiments. Studies in the first
flow include methods for sequence alignment, gene finding, RNA expression,
protein expression, prediction of protein 3D structure, population genetics, and
modelling of genetic network. The second flow begins with a hypothesis (drawn
by scanning molecular biology databases), followed by a plan to collect data,
execution of an experiment and analysis of the results.
Bioinformatics has gained prominence recently because biologists can now
collect huge amount of data by using high through put techniques. Large number
of relevant databases are available on the internet and a number of software
tools and techniques are available for data analysis. Algorithms which have
been implemented in these software are based on a number of mathematical and
statistical frameworks. Prominent among these are : Dynamic programming,
Discriminant analysis, Neural network, Markov model, Multivariate analysis,
and it couple of Machine learning techniques. |
| URI: | http://hdl.handle.net/123456789/512 |
| ISBN: | 81-7764-968-X |
| Appears in Collections: | Biometry and Statistics
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Files in This Item:
| File |
Description |
Size | Format |
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| statistics advances in biosciences&bioinfomatics-2006-25-34.PDF | | 6866Kb | Adobe PDF | View/Open |
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