000 02244nam a22003738i 4500
001 CR9780511790492
003 UkCbUP
005 20220711202545.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 100611s1998||||enk o ||1 0|eng|d
020 _a9780511790492 (ebook)
020 _z9780521620413 (hardback)
020 _z9780521629713 (paperback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 4 _aQP620
_b.D87 1998
082 0 0 _a572.8/633
_221
100 1 _aDurbin, Richard,
_eauthor.
_94600
245 1 0 _aBiological sequence analysis :
_bprobabalistic models of proteins and nucleic acids /
_cRichard Durbin [and three others].
264 1 _aCambridge :
_bCambridge University Press,
_c1998.
300 _a1 online resource (xi, 356 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520 _aProbabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
650 0 _aNucleotide sequence
_xStatistical methods.
_94601
650 0 _aAmino acid sequence
_xStatistical methods.
_94602
650 0 _aNumerical analysis.
_94603
650 0 _aProbabilities.
_94604
776 0 8 _iPrint version:
_z9780521620413
856 4 0 _uhttps://doi.org/10.1017/CBO9780511790492
942 _cEBK
999 _c68303
_d68303