FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3138032944> ?p ?o ?g. }

• W3138032944 endingPage "114876" @default.
• W3138032944 startingPage "114876" @default.
• W3138032944 abstract "Prediction of protein–protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. Firstly, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), amino acid composition position-specific scoring matrix (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, we ensemble XGBoost, random forest, and extremely randomized trees to construct deep forest model via cascade architecture for PPIs prediction (GcForest-PPI). Benchmark experiments reveal that the proposed approach outperforms other state-of-the-art predictors on Saccharomyces cerevisiae and Helicobacter pylori. We also apply GcForest-PPI on independent test sets, CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery." @default.
• W3138032944 created "2021-03-29" @default.
• W3138032944 creator A5017344694 @default.
• W3138032944 creator A5038117190 @default.
• W3138032944 creator A5048473316 @default.
• W3138032944 creator A5056199285 @default.
• W3138032944 creator A5064548129 @default.
• W3138032944 creator A5075590567 @default.
• W3138032944 date "2021-08-01" @default.
• W3138032944 modified "2023-10-10" @default.
• W3138032944 title "Prediction of protein–protein interactions based on elastic net and deep forest" @default.
• W3138032944 cites W1579194600 @default.
• W3138032944 cites W1817561967 @default.
• W3138032944 cites W1886978693 @default.
• W3138032944 cites W1918372212 @default.
• W3138032944 cites W1982267716 @default.
• W3138032944 cites W1984208669 @default.
• W3138032944 cites W1985919127 @default.
• W3138032944 cites W1988790447 @default.
• W3138032944 cites W2027509786 @default.
• W3138032944 cites W2040941311 @default.
• W3138032944 cites W2047031939 @default.
• W3138032944 cites W2050592587 @default.
• W3138032944 cites W2053186076 @default.
• W3138032944 cites W2056132907 @default.
• W3138032944 cites W2076710572 @default.
• W3138032944 cites W2078162414 @default.
• W3138032944 cites W2080922998 @default.
• W3138032944 cites W2087322782 @default.
• W3138032944 cites W2089468765 @default.
• W3138032944 cites W2097698606 @default.
• W3138032944 cites W2101181377 @default.
• W3138032944 cites W2101699812 @default.
• W3138032944 cites W2108211735 @default.
• W3138032944 cites W2108712612 @default.
• W3138032944 cites W2112632190 @default.
• W3138032944 cites W2112796928 @default.
• W3138032944 cites W2116089841 @default.
• W3138032944 cites W2119583098 @default.
• W3138032944 cites W2122825543 @default.
• W3138032944 cites W2132582966 @default.
• W3138032944 cites W2134672568 @default.
• W3138032944 cites W2135002317 @default.
• W3138032944 cites W2140095548 @default.
• W3138032944 cites W2142694622 @default.
• W3138032944 cites W2145957695 @default.
• W3138032944 cites W2146982832 @default.
• W3138032944 cites W2150452800 @default.
• W3138032944 cites W2152705149 @default.
• W3138032944 cites W2162392441 @default.
• W3138032944 cites W2170798597 @default.
• W3138032944 cites W2172049947 @default.
• W3138032944 cites W2280888683 @default.
• W3138032944 cites W2338910273 @default.
• W3138032944 cites W2340589369 @default.
• W3138032944 cites W2345139079 @default.
• W3138032944 cites W2517416483 @default.
• W3138032944 cites W2524367868 @default.
• W3138032944 cites W2547150868 @default.
• W3138032944 cites W2616246685 @default.
• W3138032944 cites W2620675477 @default.
• W3138032944 cites W2750547662 @default.
• W3138032944 cites W2755290882 @default.
• W3138032944 cites W2793168264 @default.
• W3138032944 cites W2802989292 @default.
• W3138032944 cites W2804331675 @default.
• W3138032944 cites W2807846544 @default.
• W3138032944 cites W2808071711 @default.
• W3138032944 cites W2808963783 @default.
• W3138032944 cites W2890911678 @default.
• W3138032944 cites W2900868822 @default.
• W3138032944 cites W2901314256 @default.
• W3138032944 cites W2902907165 @default.
• W3138032944 cites W2904726360 @default.
• W3138032944 cites W2904742480 @default.
• W3138032944 cites W2907063321 @default.
• W3138032944 cites W2911964244 @default.
• W3138032944 cites W2936781144 @default.
• W3138032944 cites W2944679105 @default.
• W3138032944 cites W2950389803 @default.
• W3138032944 cites W2950742983 @default.
• W3138032944 cites W2957436444 @default.
• W3138032944 cites W2997708032 @default.
• W3138032944 cites W3043299403 @default.
• W3138032944 cites W3048991675 @default.
• W3138032944 cites W4232714830 @default.
• W3138032944 cites W4239510810 @default.
• W3138032944 cites W76924599 @default.
• W3138032944 doi "https://doi.org/10.1016/j.eswa.2021.114876" @default.
• W3138032944 hasPublicationYear "2021" @default.
• W3138032944 type Work @default.
• W3138032944 sameAs 3138032944 @default.
• W3138032944 citedByCount "38" @default.
• W3138032944 countsByYear W31380329442021 @default.
• W3138032944 countsByYear W31380329442022 @default.
• W3138032944 countsByYear W31380329442023 @default.
• W3138032944 crossrefType "journal-article" @default.
• W3138032944 hasAuthorship W3138032944A5017344694 @default.

• next