05463nam 2200661Ia 450 991014273260332120170815121616.01-280-23868-297866102386820-470-02645-60-470-02057-1(CKB)1000000000330786(EBL)239459(OCoLC)475950782(SSID)ssj0000111898(PQKBManifestationID)11125314(PQKBTitleCode)TC0000111898(PQKBWorkID)10081627(PQKB)10910090(MiAaPQ)EBC239459(EXLCZ)99100000000033078620031217d2004 uy 0engur|n|---|||||txtccrBioinformatics, biocomputing and Perl[electronic resource] an introduction to bioinformatics computing skills and practice /Michael Moorhouse, Paul BarryChichester ;Hoboken, NJ Wileyc20041 online resource (507 p.)Description based upon print version of record.0-470-85331-X Includes bibliographical references (p. [461]-466) and index.Bioinformatics Biocomputing and Perl; Contents; Preface; 1 Setting the Biological Scene; 1.1 Introducing Biological Sequence Analysis; 1.2 Protein and Polypeptides; 1.3 Generalised Models and their Use; 1.4 The Central Dogma of Molecular Biology; 1.4.1 Transcription; 1.4.2 Translation; 1.5 Genome Sequencing; 1.5.1 Sequence assembly; 1.6 The Example DNA-gene-protein system we will use; Where to from Here; 2 Setting the Technological Scene; 2.1 The Layers of Technology; 2.1.1 From passive user to active developer; 2.2 Finding perl; 2.2.1 Checking for perl; Where to from HereI Working with Perl3 The Basics; 3.1 Let's Get Started!; 3.1.1 Running Perl programs; 3.1.2 Syntax and semantics; 3.1.3 Program: run thyself!; 3.2 Iteration; 3.2.1 Using the Perl while construct; 3.3 More Iterations; 3.3.1 Introducing variable containers; 3.3.2 Variable containers and loops; 3.4 Selection; 3.4.1 Using the Perl if construct; 3.5 There Really is MTOWTDI; 3.6 Processing Data Files; 3.6.1 Asking getlines to do more; 3.7 Introducing Patterns; Where to from Here; The Maxims Repeated; 4 Places to Put Things; 4.1 Beyond Scalars; 4.2 Arrays: Associating Data with Numbers4.2.1 Working with array elements4.2.2 How big is the array?; 4.2.3 Adding elements to an array; 4.2.4 Removing elements from an array; 4.2.5 Slicing arrays; 4.2.6 Pushing, popping, shifting and unshifting; 4.2.7 Processing every element in an array; 4.2.8 Making lists easier to work with; 4.3 Hashes: Associating Data with Words; 4.3.1 Working with hash entries; 4.3.2 How big is the hash?; 4.3.3 Adding entries to a hash; 4.3.4 Removing entries from a hash; 4.3.5 Slicing hashes; 4.3.6 Working with hash entries: a complete example; 4.3.7 Processing every entry in a hash; Where to from HereThe Maxims Repeated5 Getting Organised; 5.1 Named Blocks; 5.2 Introducing Subroutines; 5.2.1 Calling subroutines; 5.3 Creating Subroutines; 5.3.1 Processing parameters; 5.3.2 Better processing of parameters; 5.3.3 Even better processing of parameters; 5.3.4 A more flexible drawline subroutine; 5.3.5 Returning results; 5.4 Visibility and Scope; 5.4.1 Using private variables; 5.4.2 Using global variables properly; 5.4.3 The final version of drawline; 5.5 In-built Subroutines; 5.6 Grouping and Reusing Subroutines; 5.6.1 Modules; 5.7 The Standard Modules; 5.8 CPAN: The Module Repository5.8.1 Searching CPAN5.8.2 Installing a CPAN module manually; 5.8.3 Installing a CPAN module automatically; 5.8.4 A final word on CPAN modules; Where to from Here; The Maxims Repeated; 6 About Files; 6.1 I/O: Input and Output; 6.1.1 The standard streams: STDIN, STDOUT and STDERR; 6.2 Reading Files; 6.2.1 Determining the disk-file names; 6.2.2 Opening the named disk-files; 6.2.3 Reading a line from each of the disk-files; 6.2.4 Putting it all together; 6.2.5 Slurping; 6.3 Writing Files; 6.3.1 Redirecting output; 6.3.2 Variable interpolation; 6.4 Chopping and Chomping; Where to from HereThe Maxims RepeatedBioinformatics, Biocomputing and Perl presents a modern introduction to bioinformatics computing skills and practice. Structuring its presentation around four main areas of study, this book covers the skills vital to the day-to-day activities of today's bioinformatician. Each chapter contains a series of maxims designed to highlight key points and there are exercises to supplement and cement the introduced material. Working with Perl presents an extended tutorial introduction to programming through Perl, the premier programming technology of the bioinformatics community. Even tBioinformaticsComputational biologyPerl (Computer program language)Electronic books.Bioinformatics.Computational biology.Perl (Computer program language)570.285570.285571262Moorhouse Michael962272Barry Paul1966-962273MiAaPQMiAaPQMiAaPQBOOK9910142732603321Bioinformatics, biocomputing and Perl2181906UNINA02826oam 2200517 450 991043803470332120190911103512.01-4614-8508-810.1007/978-1-4614-8508-7(OCoLC)862941167(MiFhGG)GVRL6XYK(EXLCZ)99371000000002429620130722d2013 uy 0engurun|---uuuuatxtccrMean field games and mean field type control theory /Alain Bensoussan, Jens Frehse, Phillip Yam1st ed. 2013.New York :Springer,2013.1 online resource (x, 128 pages)SpringerBriefs in Mathematics,2191-8198"ISSN: 2191-8198.""ISSN: 2191-8201 (electronic)."1-4614-8507-X Includes bibliographical references and index.Introduction -- General Presentation of Mean Field Control Problems -- Discussion of the Mean Field game -- Discussion of the Mean Field Type Control -- Approximation of Nash Games with a large number of players -- Linear Quadratic Models -- Stationary Problems- Different Populations -- Nash differential games with Mean Field effect.Mean field games and Mean field type control introduce new problems in Control Theory. The terminology “games” may be confusing. In fact they are control problems, in the sense that one is interested in a single decision maker, whom we can call the representative agent. However, these problems are not standard, since both the evolution of the state and the objective functional is influenced but terms which are not directly related to the state or the control of the decision maker. They are however, indirectly related to him, in the sense that they model a very large community of agents similar to the representative agent. All the agents behave similarly and impact the representative agent. However, because of the large number an aggregation effect takes place. The interesting consequence is that the impact of the community can be modeled by a mean field term, but when this is done, the problem is reduced to a control problem. .SpringerBriefs in mathematics.Mean field theoryControl theoryGame theoryMean field theory.Control theory.Game theory.515.642Bensoussan Alainauthttp://id.loc.gov/vocabulary/relators/aut14378Frehse J(Jens),Yam PhillipMiFhGGMiFhGGBOOK9910438034703321Mean Field Games and Mean Field Type Control Theory2506832UNINA