000725340 000__ 02701cam\a2200505Ii\4500 000725340 001__ 725340 000725340 005__ 20230306140633.0 000725340 006__ m\\\\\o\\d\\\\\\\\ 000725340 007__ cr\cn\nnnunnun 000725340 008__ 150126s2015\\\\sz\a\\\\ob\\\\000\0\eng\d 000725340 019__ $$a908041811 000725340 020__ $$a9783319149141$$qelectronic book 000725340 020__ $$a3319149148$$qelectronic book 000725340 020__ $$z9783319149134 000725340 0247_ $$a10.1007/978-3-319-14914-1$$2doi 000725340 035__ $$aSP(OCoLC)ocn900723995 000725340 035__ $$aSP(OCoLC)900723995$$z(OCoLC)908041811 000725340 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dN$T$$dUPM$$dCOO$$dOCLCF$$dBTCTA$$dIDEBK$$dCDX$$dE7B$$dEBLCP$$dYDXCP$$dVLB 000725340 049__ $$aISEA 000725340 050_4 $$aQA274.45$$b.X8 2015eb 000725340 08204 $$a519.2/3$$223 000725340 1001_ $$aXu, Jinbo,$$eauthor. 000725340 24510 $$aProtein homology detection through alignment of Markov random fields$$h[electronic resource] :$$busing MRFalign /$$cJinbo Xu, Sheng Wang, Jianzhu Ma. 000725340 264_1 $$aCham :$$bSpringer,$$c2015. 000725340 300__ $$a1 online resource (viii, 51 pages) :$$billustrations (some color). 000725340 336__ $$atext$$btxt$$2rdacontent 000725340 337__ $$acomputer$$bc$$2rdamedia 000725340 338__ $$aonline resource$$bcr$$2rdacarrier 000725340 4901_ $$aSpringerBriefs in Computer Science,$$x2191-5768 000725340 504__ $$aIncludes bibliographical references. 000725340 5050_ $$aIntroduction -- Method -- Software -- Experiments and Results -- Conclusion. 000725340 506__ $$aAccess limited to authorized users. 000725340 520__ $$aThis work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method. 000725340 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 4, 2015). 000725340 650_0 $$aMarkov random fields. 000725340 650_0 $$aSequence alignment (Bioinformatics) 000725340 650_0 $$aBioinformatics. 000725340 7001_ $$aWang, Sheng,$$eauthor. 000725340 7001_ $$aMa, Jianzhu,$$eauthor. 000725340 77608 $$iPrint version:$$z9783319149134 000725340 830_0 $$aSpringerBriefs in computer science. 000725340 852__ $$bebk 000725340 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-14914-1$$zOnline Access$$91397441.1 000725340 909CO $$ooai:library.usi.edu:725340$$pGLOBAL_SET 000725340 980__ $$aEBOOK 000725340 980__ $$aBIB 000725340 982__ $$aEbook 000725340 983__ $$aOnline 000725340 994__ $$a92$$bISE