Matches in SemOpenAlex for { <https://semopenalex.org/work/W3094502431> ?p ?o ?g. }
- W3094502431 abstract "Abstract Topic modelling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modelling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample." @default.
- W3094502431 created "2020-10-29" @default.
- W3094502431 creator A5031508753 @default.
- W3094502431 creator A5032497784 @default.
- W3094502431 creator A5090221395 @default.
- W3094502431 date "2020-10-21" @default.
- W3094502431 modified "2023-10-14" @default.
- W3094502431 title "A topic modelling analysis of TCGA breast and lung cancer transcriptomic data" @default.
- W3094502431 cites W1880262756 @default.
- W3094502431 cites W1908864745 @default.
- W3094502431 cites W1966327575 @default.
- W3094502431 cites W1968426398 @default.
- W3094502431 cites W1969361257 @default.
- W3094502431 cites W1980509985 @default.
- W3094502431 cites W1988818350 @default.
- W3094502431 cites W1989734503 @default.
- W3094502431 cites W2016381774 @default.
- W3094502431 cites W2018838463 @default.
- W3094502431 cites W2053039860 @default.
- W3094502431 cites W2096283457 @default.
- W3094502431 cites W2097255042 @default.
- W3094502431 cites W2104426522 @default.
- W3094502431 cites W2130410032 @default.
- W3094502431 cites W2131994307 @default.
- W3094502431 cites W2132619562 @default.
- W3094502431 cites W2134662941 @default.
- W3094502431 cites W2136787567 @default.
- W3094502431 cites W2137873181 @default.
- W3094502431 cites W2150926065 @default.
- W3094502431 cites W2156527837 @default.
- W3094502431 cites W2157132621 @default.
- W3094502431 cites W2158485828 @default.
- W3094502431 cites W2166311434 @default.
- W3094502431 cites W2167105148 @default.
- W3094502431 cites W2169148563 @default.
- W3094502431 cites W2182664595 @default.
- W3094502431 cites W2230320310 @default.
- W3094502431 cites W2263948230 @default.
- W3094502431 cites W2513408092 @default.
- W3094502431 cites W2521492299 @default.
- W3094502431 cites W2522581443 @default.
- W3094502431 cites W2531871281 @default.
- W3094502431 cites W2604228316 @default.
- W3094502431 cites W2742276251 @default.
- W3094502431 cites W2762349056 @default.
- W3094502431 cites W2784871033 @default.
- W3094502431 cites W2800392236 @default.
- W3094502431 cites W2801400022 @default.
- W3094502431 cites W2810979734 @default.
- W3094502431 cites W2891131714 @default.
- W3094502431 cites W2917270815 @default.
- W3094502431 cites W2918849193 @default.
- W3094502431 cites W2952626686 @default.
- W3094502431 cites W2963459858 @default.
- W3094502431 cites W2969730347 @default.
- W3094502431 cites W3103015506 @default.
- W3094502431 cites W3125564425 @default.
- W3094502431 cites W3126033509 @default.
- W3094502431 cites W3147894994 @default.
- W3094502431 cites W4211032970 @default.
- W3094502431 doi "https://doi.org/10.1101/2020.10.19.345694" @default.
- W3094502431 hasPublicationYear "2020" @default.
- W3094502431 type Work @default.
- W3094502431 sameAs 3094502431 @default.
- W3094502431 citedByCount "0" @default.
- W3094502431 crossrefType "posted-content" @default.
- W3094502431 hasAuthorship W3094502431A5031508753 @default.
- W3094502431 hasAuthorship W3094502431A5032497784 @default.
- W3094502431 hasAuthorship W3094502431A5090221395 @default.
- W3094502431 hasBestOaLocation W30945024311 @default.
- W3094502431 hasConcept C104317684 @default.
- W3094502431 hasConcept C119857082 @default.
- W3094502431 hasConcept C121608353 @default.
- W3094502431 hasConcept C124101348 @default.
- W3094502431 hasConcept C126322002 @default.
- W3094502431 hasConcept C138885662 @default.
- W3094502431 hasConcept C143998085 @default.
- W3094502431 hasConcept C150194340 @default.
- W3094502431 hasConcept C154945302 @default.
- W3094502431 hasConcept C162317418 @default.
- W3094502431 hasConcept C2776256026 @default.
- W3094502431 hasConcept C41008148 @default.
- W3094502431 hasConcept C41895202 @default.
- W3094502431 hasConcept C521332185 @default.
- W3094502431 hasConcept C530470458 @default.
- W3094502431 hasConcept C55493867 @default.
- W3094502431 hasConcept C70721500 @default.
- W3094502431 hasConcept C71924100 @default.
- W3094502431 hasConcept C86803240 @default.
- W3094502431 hasConcept C95623464 @default.
- W3094502431 hasConceptScore W3094502431C104317684 @default.
- W3094502431 hasConceptScore W3094502431C119857082 @default.
- W3094502431 hasConceptScore W3094502431C121608353 @default.
- W3094502431 hasConceptScore W3094502431C124101348 @default.
- W3094502431 hasConceptScore W3094502431C126322002 @default.
- W3094502431 hasConceptScore W3094502431C138885662 @default.
- W3094502431 hasConceptScore W3094502431C143998085 @default.
- W3094502431 hasConceptScore W3094502431C150194340 @default.
- W3094502431 hasConceptScore W3094502431C154945302 @default.
- W3094502431 hasConceptScore W3094502431C162317418 @default.