FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2950552843> ?p ?o ?g. }

• W2950552843 abstract "ABSTRACT Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER-patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value<0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion & absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accurcy (AUC=0.93) and better revelation of disease biology. We encourage the adoption of autoencoder based deep learning method in the metabolomics research community for classification." @default.
• W2950552843 created "2019-06-27" @default.
• W2950552843 creator A5005523854 @default.
• W2950552843 creator A5010070150 @default.
• W2950552843 creator A5044953798 @default.
• W2950552843 date "2017-11-06" @default.
• W2950552843 modified "2023-09-28" @default.
• W2950552843 title "Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data" @default.
• W2950552843 cites W1546952499 @default.
• W2950552843 cites W1896429920 @default.
• W2950552843 cites W1973010974 @default.
• W2950552843 cites W1975328793 @default.
• W2950552843 cites W1975775976 @default.
• W2950552843 cites W1980412502 @default.
• W2950552843 cites W1994552886 @default.
• W2950552843 cites W1998767819 @default.
• W2950552843 cites W1999842873 @default.
• W2950552843 cites W2000263671 @default.
• W2950552843 cites W2002122781 @default.
• W2950552843 cites W2006617902 @default.
• W2950552843 cites W2015861736 @default.
• W2950552843 cites W2019458473 @default.
• W2950552843 cites W2020541351 @default.
• W2950552843 cites W2029854670 @default.
• W2950552843 cites W2039534903 @default.
• W2950552843 cites W2051522090 @default.
• W2950552843 cites W2056600319 @default.
• W2950552843 cites W2071516191 @default.
• W2950552843 cites W2072816950 @default.
• W2950552843 cites W2086816631 @default.
• W2950552843 cites W2089041399 @default.
• W2950552843 cites W2096097232 @default.
• W2950552843 cites W2101239722 @default.
• W2950552843 cites W2106378100 @default.
• W2950552843 cites W2124672439 @default.
• W2950552843 cites W2124911115 @default.
• W2950552843 cites W2125569277 @default.
• W2950552843 cites W2135274825 @default.
• W2950552843 cites W2140207977 @default.
• W2950552843 cites W2145859259 @default.
• W2950552843 cites W2155158051 @default.
• W2950552843 cites W2167976870 @default.
• W2950552843 cites W2177317049 @default.
• W2950552843 cites W2264017649 @default.
• W2950552843 cites W2290653689 @default.
• W2950552843 cites W2311607323 @default.
• W2950552843 cites W2325140777 @default.
• W2950552843 cites W2336310263 @default.
• W2950552843 cites W2345512687 @default.
• W2950552843 cites W2465446255 @default.
• W2950552843 cites W2502949459 @default.
• W2950552843 cites W2508843977 @default.
• W2950552843 cites W2520222164 @default.
• W2950552843 cites W2597990875 @default.
• W2950552843 cites W2933054875 @default.
• W2950552843 cites W3105486817 @default.
• W2950552843 doi "https://doi.org/10.1101/214254" @default.
• W2950552843 hasPublicationYear "2017" @default.
• W2950552843 type Work @default.
• W2950552843 sameAs 2950552843 @default.
• W2950552843 citedByCount "1" @default.
• W2950552843 countsByYear W29505528432021 @default.
• W2950552843 crossrefType "posted-content" @default.
• W2950552843 hasAuthorship W2950552843A5005523854 @default.
• W2950552843 hasAuthorship W2950552843A5010070150 @default.
• W2950552843 hasAuthorship W2950552843A5044953798 @default.
• W2950552843 hasBestOaLocation W29505528431 @default.
• W2950552843 hasConcept C101738243 @default.
• W2950552843 hasConcept C108583219 @default.
• W2950552843 hasConcept C119857082 @default.
• W2950552843 hasConcept C121608353 @default.
• W2950552843 hasConcept C12267149 @default.
• W2950552843 hasConcept C126322002 @default.
• W2950552843 hasConcept C153180895 @default.
• W2950552843 hasConcept C154945302 @default.
• W2950552843 hasConcept C169258074 @default.
• W2950552843 hasConcept C21565614 @default.
• W2950552843 hasConcept C41008148 @default.
• W2950552843 hasConcept C50644808 @default.
• W2950552843 hasConcept C530470458 @default.
• W2950552843 hasConcept C60644358 @default.
• W2950552843 hasConcept C69738355 @default.
• W2950552843 hasConcept C71924100 @default.
• W2950552843 hasConcept C84606932 @default.
• W2950552843 hasConcept C86803240 @default.
• W2950552843 hasConceptScore W2950552843C101738243 @default.
• W2950552843 hasConceptScore W2950552843C108583219 @default.
• W2950552843 hasConceptScore W2950552843C119857082 @default.
• W2950552843 hasConceptScore W2950552843C121608353 @default.
• W2950552843 hasConceptScore W2950552843C12267149 @default.
• W2950552843 hasConceptScore W2950552843C126322002 @default.
• W2950552843 hasConceptScore W2950552843C153180895 @default.
• W2950552843 hasConceptScore W2950552843C154945302 @default.
• W2950552843 hasConceptScore W2950552843C169258074 @default.
• W2950552843 hasConceptScore W2950552843C21565614 @default.
• W2950552843 hasConceptScore W2950552843C41008148 @default.
• W2950552843 hasConceptScore W2950552843C50644808 @default.
• W2950552843 hasConceptScore W2950552843C530470458 @default.
• W2950552843 hasConceptScore W2950552843C60644358 @default.
• W2950552843 hasConceptScore W2950552843C69738355 @default.

• next