FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2904742480> ?p ?o ?g. }

• W2904742480 endingPage "1852" @default.
• W2904742480 startingPage "1839" @default.
• W2904742480 abstract "The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use." @default.
• W2904742480 created "2018-12-22" @default.
• W2904742480 creator A5003794185 @default.
• W2904742480 creator A5006300825 @default.
• W2904742480 creator A5011415224 @default.
• W2904742480 creator A5023047996 @default.
• W2904742480 creator A5027937292 @default.
• W2904742480 creator A5085661886 @default.
• W2904742480 date "2019-12-01" @default.
• W2904742480 modified "2023-10-14" @default.
• W2904742480 title "Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure" @default.
• W2904742480 cites W1544009106 @default.
• W2904742480 cites W1564885879 @default.
• W2904742480 cites W1975875968 @default.
• W2904742480 cites W1976193075 @default.
• W2904742480 cites W1984084871 @default.
• W2904742480 cites W2001619934 @default.
• W2904742480 cites W2007965730 @default.
• W2904742480 cites W2009313526 @default.
• W2904742480 cites W2012438480 @default.
• W2904742480 cites W2015461838 @default.
• W2904742480 cites W2017675395 @default.
• W2904742480 cites W2018408116 @default.
• W2904742480 cites W2025577942 @default.
• W2904742480 cites W2031216614 @default.
• W2904742480 cites W2034537627 @default.
• W2904742480 cites W2040324575 @default.
• W2904742480 cites W2044002635 @default.
• W2904742480 cites W2057195902 @default.
• W2904742480 cites W2058708125 @default.
• W2904742480 cites W2062403173 @default.
• W2904742480 cites W2066201825 @default.
• W2904742480 cites W2068351024 @default.
• W2904742480 cites W2071128523 @default.
• W2904742480 cites W2074978477 @default.
• W2904742480 cites W2082605863 @default.
• W2904742480 cites W2087603035 @default.
• W2904742480 cites W2103951757 @default.
• W2904742480 cites W2106029302 @default.
• W2904742480 cites W2108069034 @default.
• W2904742480 cites W2109991441 @default.
• W2904742480 cites W2118872355 @default.
• W2904742480 cites W2119862467 @default.
• W2904742480 cites W2127249498 @default.
• W2904742480 cites W2129916980 @default.
• W2904742480 cites W2132292391 @default.
• W2904742480 cites W2135007932 @default.
• W2904742480 cites W2135209350 @default.
• W2904742480 cites W2139736926 @default.
• W2904742480 cites W2144000913 @default.
• W2904742480 cites W2145957695 @default.
• W2904742480 cites W2148143831 @default.
• W2904742480 cites W2153187042 @default.
• W2904742480 cites W2153838454 @default.
• W2904742480 cites W2155770902 @default.
• W2904742480 cites W2158714788 @default.
• W2904742480 cites W2159887157 @default.
• W2904742480 cites W2162011385 @default.
• W2904742480 cites W2165674132 @default.
• W2904742480 cites W2169230132 @default.
• W2904742480 cites W2169678694 @default.
• W2904742480 cites W2171227711 @default.
• W2904742480 cites W2178378573 @default.
• W2904742480 cites W2189308973 @default.
• W2904742480 cites W2233800101 @default.
• W2904742480 cites W2256553158 @default.
• W2904742480 cites W2259538443 @default.
• W2904742480 cites W2302413701 @default.
• W2904742480 cites W2343107734 @default.
• W2904742480 cites W2399392834 @default.
• W2904742480 cites W2412679507 @default.
• W2904742480 cites W2419892062 @default.
• W2904742480 cites W2546726420 @default.
• W2904742480 cites W2576041084 @default.
• W2904742480 cites W2605952223 @default.
• W2904742480 cites W2608081584 @default.
• W2904742480 cites W2612835976 @default.
• W2904742480 cites W2614433385 @default.
• W2904742480 cites W2623060321 @default.
• W2904742480 cites W2729788619 @default.
• W2904742480 cites W2750547662 @default.
• W2904742480 cites W2753550436 @default.
• W2904742480 cites W2753953057 @default.
• W2904742480 cites W2754595644 @default.
• W2904742480 cites W2770261575 @default.
• W2904742480 cites W2911964244 @default.
• W2904742480 cites W3175318380 @default.
• W2904742480 cites W363319864 @default.
• W2904742480 doi "https://doi.org/10.1016/j.ygeno.2018.12.007" @default.
• W2904742480 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30550813" @default.
• W2904742480 hasPublicationYear "2019" @default.
• W2904742480 type Work @default.
• W2904742480 sameAs 2904742480 @default.
• W2904742480 citedByCount "129" @default.
• W2904742480 countsByYear W29047424802019 @default.
• W2904742480 countsByYear W29047424802020 @default.
• W2904742480 countsByYear W29047424802021 @default.
• W2904742480 countsByYear W29047424802022 @default.

• next