LEADER 05382nam 2200697Ia 450 001 9910823657403321 005 20240402140646.0 010 $a1-281-93473-9 010 $a9786611934736 010 $a981-279-484-0 035 $a(CKB)1000000000537795 035 $a(EBL)1679392 035 $a(OCoLC)879023575 035 $a(SSID)ssj0000243610 035 $a(PQKBManifestationID)11188245 035 $a(PQKBTitleCode)TC0000243610 035 $a(PQKBWorkID)10160303 035 $a(PQKB)11205039 035 $a(MiAaPQ)EBC1679392 035 $a(WSP)00005489 035 $a(Au-PeEL)EBL1679392 035 $a(CaPaEBR)ebr10255845 035 $a(CaONFJC)MIL193473 035 $a(EXLCZ)991000000000537795 100 $a20041008d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSelected topics in post-genome knowledge discovery$b[electronic resource] /$feditor[s], Limsoon Wong, Louxin Zhang 205 $a1st ed. 210 $aSingapore $cSingapore University Press ;$aSingapore ;$aRiver Edge, NJ $cWorld Scientific Pub. Co.$dc2004 215 $a1 online resource (176 p.) 225 1 $aLecture notes series ;$vvol. 3 300 $aDescription based upon print version of record. 311 $a981-238-780-3 320 $aIncludes bibliographical references. 327 $aContents ; Foreword ; Preface ; Dynamic Programming Strategies for Analyzing Biomolecular Sequences ; 1. Introduction ; 2. Elementary Dynamic-Programming Algorithms ; 2.1. Fibonacci numbers ; 2.2. The maximum-sum substring problem ; 2.3. Longest increasing subsequence 327 $a2.4. Longest common subsequence 3. Sequence Alignment ; 3.1. Global alignment ; 3.2. Local alignment ; 3.3. Affine gap penalties ; 3.4. Space-saving strategies ; 3.5. Multiple sequence alignment ; The Representation Comparison and Prediction of Protein Pathways ; 1. Introduction 327 $a2. Online Pathway Resources 3. Pathway Representation ; 3.1. Pathway Space ; 3.2. SLIPR Format ; 4. Pathway Comparison ; 4.1. Comparing Individual Components ; 4.2. Aligning Two Pathways Using Dynamic Programming ; 4.3. Pathway Database Comparison 327 $a4.4. One Implementation: PM_search Documentation 5. Orthologous Pathway Prediction ; 6. Discussion ; 6.1. Theoretical Issues on Evolutionary Study of Pathways ; 6.2. Establishing a Relational Pathway Database and its Web Interfaces ; 6.3. Pathway Prediction and Beyond 327 $aGene Network Inference and Biopathway Modeling 1. Introduction ; 2. Gene Network Inference from Microarray Data ; 2.1. Boolean Network Model ; 2.2. Bayesian Network Model ; 3. Modeling and Simulation ; 3.1. Architecture for Biopathway Modeling ; 3.2. How to Model Biopathways 327 $a3.3. Genomic Object Net and Biopathway Databases Towards Simulation 330 $a The Institute for Mathematical Sciences at the National University of Singapore organized a program on "Post-Genome Knowledge Discovery" from January to June 2002. The program focused on the computational and statistical analysis of sequences and genetics, and the mathematical modeling of complex biological interactions, which are critical to the accurate annotation of genomic sequences, the study of the interplay between genes and proteins, and the study of the genetic variability of species. As part of the program, tutorials for graduate students and newcomers to this transdisciplinary area 410 0$aLecture notes series (National University of Singapore. Institute for Mathematical Sciences) ;$vv. 3. 606 $aGenetics$xMathematics 606 $aGenetics 615 0$aGenetics$xMathematics. 615 0$aGenetics. 676 $a572.8 676 $a572.80285 701 $aWong$b Limsoon$f1965-$01650161 701 $aZhang$b Louxin$01690960 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823657403321 996 $aSelected topics in post-genome knowledge discovery$94067008 997 $aUNINA