FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2901775589> ?p ?o ?g. }

• W2901775589 endingPage "2215" @default.
• W2901775589 startingPage "2208" @default.
• W2901775589 abstract "Abstract Motivation Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. Results We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W2901775589 created "2018-11-29" @default.
• W2901775589 creator A5035334094 @default.
• W2901775589 creator A5074156910 @default.
• W2901775589 creator A5080412387 @default.
• W2901775589 creator A5087868319 @default.
• W2901775589 creator A5089349897 @default.
• W2901775589 date "2018-11-16" @default.
• W2901775589 modified "2023-09-30" @default.
• W2901775589 title "Semi-supervised learning of Hidden Markov Models for biological sequence analysis" @default.
• W2901775589 cites W1551380734 @default.
• W2901775589 cites W1605593725 @default.
• W2901775589 cites W1821139715 @default.
• W2901775589 cites W1938173378 @default.
• W2901775589 cites W1939505221 @default.
• W2901775589 cites W1967639437 @default.
• W2901775589 cites W1971147414 @default.
• W2901775589 cites W1977503336 @default.
• W2901775589 cites W1988171432 @default.
• W2901775589 cites W1988284480 @default.
• W2901775589 cites W2006201402 @default.
• W2901775589 cites W2018584324 @default.
• W2901775589 cites W2034962735 @default.
• W2901775589 cites W2053943711 @default.
• W2901775589 cites W2096991417 @default.
• W2901775589 cites W2097089247 @default.
• W2901775589 cites W2097870001 @default.
• W2901775589 cites W2101210369 @default.
• W2901775589 cites W2111119053 @default.
• W2901775589 cites W2123186393 @default.
• W2901775589 cites W2125838338 @default.
• W2901775589 cites W2129343699 @default.
• W2901775589 cites W2130694895 @default.
• W2901775589 cites W2131778683 @default.
• W2901775589 cites W2146629423 @default.
• W2901775589 cites W2150853746 @default.
• W2901775589 cites W2152005244 @default.
• W2901775589 cites W2152748513 @default.
• W2901775589 cites W2152770371 @default.
• W2901775589 cites W2154218709 @default.
• W2901775589 cites W2158623906 @default.
• W2901775589 cites W2160355390 @default.
• W2901775589 cites W2161562001 @default.
• W2901775589 cites W2510407459 @default.
• W2901775589 cites W2528315959 @default.
• W2901775589 cites W2765695438 @default.
• W2901775589 cites W2886471703 @default.
• W2901775589 cites W4245668478 @default.
• W2901775589 doi "https://doi.org/10.1093/bioinformatics/bty910" @default.
• W2901775589 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30445435" @default.
• W2901775589 hasPublicationYear "2018" @default.
• W2901775589 type Work @default.
• W2901775589 sameAs 2901775589 @default.
• W2901775589 citedByCount "17" @default.
• W2901775589 countsByYear W29017755892020 @default.
• W2901775589 countsByYear W29017755892021 @default.
• W2901775589 countsByYear W29017755892022 @default.
• W2901775589 countsByYear W29017755892023 @default.
• W2901775589 crossrefType "journal-article" @default.
• W2901775589 hasAuthorship W2901775589A5035334094 @default.
• W2901775589 hasAuthorship W2901775589A5074156910 @default.
• W2901775589 hasAuthorship W2901775589A5080412387 @default.
• W2901775589 hasAuthorship W2901775589A5087868319 @default.
• W2901775589 hasAuthorship W2901775589A5089349897 @default.
• W2901775589 hasConcept C105795698 @default.
• W2901775589 hasConcept C119857082 @default.
• W2901775589 hasConcept C136389625 @default.
• W2901775589 hasConcept C153180895 @default.
• W2901775589 hasConcept C154945302 @default.
• W2901775589 hasConcept C162324750 @default.
• W2901775589 hasConcept C176217482 @default.
• W2901775589 hasConcept C177264268 @default.
• W2901775589 hasConcept C182081679 @default.
• W2901775589 hasConcept C199360897 @default.
• W2901775589 hasConcept C21547014 @default.
• W2901775589 hasConcept C23224414 @default.
• W2901775589 hasConcept C2776145971 @default.
• W2901775589 hasConcept C2778112365 @default.
• W2901775589 hasConcept C33923547 @default.
• W2901775589 hasConcept C41008148 @default.
• W2901775589 hasConcept C49781872 @default.
• W2901775589 hasConcept C49937458 @default.
• W2901775589 hasConcept C50644808 @default.
• W2901775589 hasConcept C54355233 @default.
• W2901775589 hasConcept C58973888 @default.
• W2901775589 hasConcept C8038995 @default.
• W2901775589 hasConcept C86803240 @default.
• W2901775589 hasConcept C9357733 @default.
• W2901775589 hasConceptScore W2901775589C105795698 @default.
• W2901775589 hasConceptScore W2901775589C119857082 @default.
• W2901775589 hasConceptScore W2901775589C136389625 @default.
• W2901775589 hasConceptScore W2901775589C153180895 @default.
• W2901775589 hasConceptScore W2901775589C154945302 @default.
• W2901775589 hasConceptScore W2901775589C162324750 @default.
• W2901775589 hasConceptScore W2901775589C176217482 @default.
• W2901775589 hasConceptScore W2901775589C177264268 @default.
• W2901775589 hasConceptScore W2901775589C182081679 @default.
• W2901775589 hasConceptScore W2901775589C199360897 @default.

• next