FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3212859544> ?p ?o ?g. }

• W3212859544 abstract "Amyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer's disease, Parkinson's disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions.To address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions.The advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/." @default.
• W3212859544 created "2021-11-22" @default.
• W3212859544 creator A5001374995 @default.
• W3212859544 creator A5008860042 @default.
• W3212859544 creator A5024962900 @default.
• W3212859544 creator A5031311056 @default.
• W3212859544 creator A5037131488 @default.
• W3212859544 date "2021-11-09" @default.
• W3212859544 modified "2023-10-06" @default.
• W3212859544 title "ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition" @default.
• W3212859544 cites W1972798923 @default.
• W3212859544 cites W1991244466 @default.
• W3212859544 cites W2012108346 @default.
• W3212859544 cites W2031752414 @default.
• W3212859544 cites W2033853317 @default.
• W3212859544 cites W2040042139 @default.
• W3212859544 cites W2043614254 @default.
• W3212859544 cites W2043876754 @default.
• W3212859544 cites W2043899474 @default.
• W3212859544 cites W2057709942 @default.
• W3212859544 cites W2080831965 @default.
• W3212859544 cites W2080880014 @default.
• W3212859544 cites W2095782512 @default.
• W3212859544 cites W2100630099 @default.
• W3212859544 cites W2105908180 @default.
• W3212859544 cites W2110686214 @default.
• W3212859544 cites W2117537971 @default.
• W3212859544 cites W2118943251 @default.
• W3212859544 cites W2121543180 @default.
• W3212859544 cites W2132292391 @default.
• W3212859544 cites W2133425057 @default.
• W3212859544 cites W2138259999 @default.
• W3212859544 cites W2141057671 @default.
• W3212859544 cites W2145957695 @default.
• W3212859544 cites W2148472024 @default.
• W3212859544 cites W2157873167 @default.
• W3212859544 cites W2169152986 @default.
• W3212859544 cites W2470414691 @default.
• W3212859544 cites W2588614855 @default.
• W3212859544 cites W2592644437 @default.
• W3212859544 cites W2593867025 @default.
• W3212859544 cites W2761583818 @default.
• W3212859544 cites W2770001620 @default.
• W3212859544 cites W2780936345 @default.
• W3212859544 cites W2792533056 @default.
• W3212859544 cites W2806146459 @default.
• W3212859544 cites W2896636431 @default.
• W3212859544 cites W2900329012 @default.
• W3212859544 cites W2903705089 @default.
• W3212859544 cites W2923490814 @default.
• W3212859544 cites W2947612926 @default.
• W3212859544 cites W2955640369 @default.
• W3212859544 cites W2961448903 @default.
• W3212859544 cites W2972174635 @default.
• W3212859544 cites W2972223935 @default.
• W3212859544 cites W2974802108 @default.
• W3212859544 cites W2977235223 @default.
• W3212859544 cites W2981438385 @default.
• W3212859544 cites W2985856177 @default.
• W3212859544 cites W2992607180 @default.
• W3212859544 cites W2993604350 @default.
• W3212859544 cites W2994989159 @default.
• W3212859544 cites W3001586597 @default.
• W3212859544 cites W3004222371 @default.
• W3212859544 cites W3004263072 @default.
• W3212859544 cites W3008014363 @default.
• W3212859544 cites W3012664382 @default.
• W3212859544 cites W3022704604 @default.
• W3212859544 cites W3024172273 @default.
• W3212859544 cites W3032843171 @default.
• W3212859544 cites W3043366948 @default.
• W3212859544 cites W3043901899 @default.
• W3212859544 cites W3044776631 @default.
• W3212859544 cites W3046868072 @default.
• W3212859544 cites W3093610272 @default.
• W3212859544 cites W3094567318 @default.
• W3212859544 cites W3110272794 @default.
• W3212859544 cites W3115890771 @default.
• W3212859544 cites W3134438536 @default.
• W3212859544 cites W3135521497 @default.
• W3212859544 doi "https://doi.org/10.1186/s12859-021-04446-4" @default.
• W3212859544 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8579573" @default.
• W3212859544 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34753427" @default.
• W3212859544 hasPublicationYear "2021" @default.
• W3212859544 type Work @default.
• W3212859544 sameAs 3212859544 @default.
• W3212859544 citedByCount "7" @default.
• W3212859544 countsByYear W32128595442022 @default.
• W3212859544 countsByYear W32128595442023 @default.
• W3212859544 crossrefType "journal-article" @default.
• W3212859544 hasAuthorship W3212859544A5001374995 @default.
• W3212859544 hasAuthorship W3212859544A5008860042 @default.
• W3212859544 hasAuthorship W3212859544A5024962900 @default.
• W3212859544 hasAuthorship W3212859544A5031311056 @default.
• W3212859544 hasAuthorship W3212859544A5037131488 @default.
• W3212859544 hasBestOaLocation W32128595441 @default.
• W3212859544 hasConcept C154945302 @default.
• W3212859544 hasConcept C167392928 @default.
• W3212859544 hasConcept C169258074 @default.
• W3212859544 hasConcept C185592680 @default.

• next