FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2034962735> ?p ?o ?g. }

• W2034962735 endingPage "137" @default.
• W2034962735 startingPage "128" @default.
• W2034962735 abstract "It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both alpha-helical and beta-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of alpha-helical membrane proteins perform slightly better than that of beta-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a meta-analysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors." @default.
• W2034962735 created "2016-06-24" @default.
• W2034962735 creator A5005261625 @default.
• W2034962735 creator A5023208593 @default.
• W2034962735 creator A5035334094 @default.
• W2034962735 date "2009-09-01" @default.
• W2034962735 modified "2023-09-27" @default.
• W2034962735 title "How Many 3D Structures Do We Need to Train a Predictor?" @default.
• W2034962735 cites W1491040459 @default.
• W2034962735 cites W1506812273 @default.
• W2034962735 cites W1554521867 @default.
• W2034962735 cites W1605593725 @default.
• W2034962735 cites W1821139715 @default.
• W2034962735 cites W1939505221 @default.
• W2034962735 cites W1963550288 @default.
• W2034962735 cites W1966849089 @default.
• W2034962735 cites W1969576316 @default.
• W2034962735 cites W1976005460 @default.
• W2034962735 cites W1982500800 @default.
• W2034962735 cites W1985818354 @default.
• W2034962735 cites W1987328021 @default.
• W2034962735 cites W2002272439 @default.
• W2034962735 cites W2012515247 @default.
• W2034962735 cites W2013734705 @default.
• W2034962735 cites W2026649417 @default.
• W2034962735 cites W2027837654 @default.
• W2034962735 cites W2030636556 @default.
• W2034962735 cites W2044999719 @default.
• W2034962735 cites W2046192291 @default.
• W2034962735 cites W2047544230 @default.
• W2034962735 cites W2048357129 @default.
• W2034962735 cites W2052450063 @default.
• W2034962735 cites W2053621743 @default.
• W2034962735 cites W2056402029 @default.
• W2034962735 cites W2057157558 @default.
• W2034962735 cites W2057289558 @default.
• W2034962735 cites W2072649575 @default.
• W2034962735 cites W2073739993 @default.
• W2034962735 cites W2075651068 @default.
• W2034962735 cites W2086403919 @default.
• W2034962735 cites W2087009455 @default.
• W2034962735 cites W2091726717 @default.
• W2034962735 cites W2095450147 @default.
• W2034962735 cites W2098464924 @default.
• W2034962735 cites W2100063856 @default.
• W2034962735 cites W2100890997 @default.
• W2034962735 cites W2104972430 @default.
• W2034962735 cites W2106882534 @default.
• W2034962735 cites W2107432340 @default.
• W2034962735 cites W2111193871 @default.
• W2034962735 cites W2112618708 @default.
• W2034962735 cites W2113119663 @default.
• W2034962735 cites W2114107538 @default.
• W2034962735 cites W2119423166 @default.
• W2034962735 cites W2122945697 @default.
• W2034962735 cites W2128752162 @default.
• W2034962735 cites W2128801478 @default.
• W2034962735 cites W2129838159 @default.
• W2034962735 cites W2131801377 @default.
• W2034962735 cites W2131871781 @default.
• W2034962735 cites W2134299061 @default.
• W2034962735 cites W2136572370 @default.
• W2034962735 cites W2137095432 @default.
• W2034962735 cites W2137995988 @default.
• W2034962735 cites W2138969498 @default.
• W2034962735 cites W2139582206 @default.
• W2034962735 cites W2140678125 @default.
• W2034962735 cites W2147351132 @default.
• W2034962735 cites W2147905080 @default.
• W2034962735 cites W2148395120 @default.
• W2034962735 cites W2148557779 @default.
• W2034962735 cites W2152064613 @default.
• W2034962735 cites W2152748513 @default.
• W2034962735 cites W2153187042 @default.
• W2034962735 cites W2156798505 @default.
• W2034962735 cites W2158714788 @default.
• W2034962735 cites W2159599297 @default.
• W2034962735 cites W2159940018 @default.
• W2034962735 doi "https://doi.org/10.1016/s1672-0229(08)60041-8" @default.
• W2034962735 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5054404" @default.
• W2034962735 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19944385" @default.
• W2034962735 hasPublicationYear "2009" @default.
• W2034962735 type Work @default.
• W2034962735 sameAs 2034962735 @default.
• W2034962735 citedByCount "4" @default.
• W2034962735 countsByYear W20349627352017 @default.
• W2034962735 countsByYear W20349627352018 @default.
• W2034962735 crossrefType "journal-article" @default.
• W2034962735 hasAuthorship W2034962735A5005261625 @default.
• W2034962735 hasAuthorship W2034962735A5023208593 @default.
• W2034962735 hasAuthorship W2034962735A5035334094 @default.
• W2034962735 hasBestOaLocation W20349627351 @default.
• W2034962735 hasConcept C11413529 @default.
• W2034962735 hasConcept C119857082 @default.
• W2034962735 hasConcept C124101348 @default.
• W2034962735 hasConcept C144647389 @default.
• W2034962735 hasConcept C153180895 @default.
• W2034962735 hasConcept C154945302 @default.

• next