FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2899285308> ?p ?o ?g. }

• W2899285308 abstract "Abstract N6-methyladenosine (m 6 A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m 6 A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m 6 A levels are controlled and whether and how regulation of m 6 A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m 6 A-regulated genes and m 6 A-associated disease, which includes Deep-m 6 A, the first model for detecting condition-specific m 6 A sites from MeRIP-Seq data with a single base resolution using deep learning and a new network-based pipeline that prioritizes functional significant m 6 A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m 6 A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m 6 A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m 6 A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m 6 A regulatory functions and its roles in diseases. Author summary The goal of this work is to identify functional significant m 6 A-regulated genes and m 6 A-associated diseases from analyzing an extensive collection of MeRIP-seq data. To achieve this, we first developed Deep-m 6 A, a CNN model for single-base m 6 A prediction. To our knowledge, this is the first condition-specific single-base m 6 A site prediction model that combines mRNA sequence feature and MeRIP-Seq data. The 10-fold cross-validation and test on an independent dataset showthat Deep-m 6 A outperformed two sequence-based models. We applied Deep-m 6 A followed by network-based analysis using HotNet2 and RWRH to 75 human MeRIP-Seq samples from various cells and tissue under different conditions to globally detect m 6 A-regulated genes and further predict m 6 A mediated functions and associated diseases. This is also to our knowledge the first attempt to predict m 6 A functions and associated diseases using only computational methods in a global manner on a large number of human MeRIP-Seq samples. The predicted functions and diseases show considerable consistent with those reported in the literature, which demonstrated the power of our proposed pipeline to predict potential m 6 A mediated functions and associated diseases." @default.
• W2899285308 created "2018-11-09" @default.
• W2899285308 creator A5009026385 @default.
• W2899285308 creator A5043640443 @default.
• W2899285308 creator A5057312938 @default.
• W2899285308 creator A5060017240 @default.
• W2899285308 creator A5061712236 @default.
• W2899285308 creator A5062986831 @default.
• W2899285308 creator A5086402061 @default.
• W2899285308 date "2018-11-04" @default.
• W2899285308 modified "2023-10-01" @default.
• W2899285308 title "Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods" @default.
• W2899285308 cites W1186776093 @default.
• W2899285308 cites W1933156045 @default.
• W2899285308 cites W1941339056 @default.
• W2899285308 cites W1973541913 @default.
• W2899285308 cites W1997182468 @default.
• W2899285308 cites W1998519606 @default.
• W2899285308 cites W2009019945 @default.
• W2899285308 cites W2009308529 @default.
• W2899285308 cites W2011131953 @default.
• W2899285308 cites W2015217788 @default.
• W2899285308 cites W2022129953 @default.
• W2899285308 cites W2029857102 @default.
• W2899285308 cites W2037754272 @default.
• W2899285308 cites W2042003093 @default.
• W2899285308 cites W2044676948 @default.
• W2899285308 cites W2065847836 @default.
• W2899285308 cites W2066972125 @default.
• W2899285308 cites W2078964320 @default.
• W2899285308 cites W2080310417 @default.
• W2899285308 cites W2088891952 @default.
• W2899285308 cites W2091235283 @default.
• W2899285308 cites W2100305481 @default.
• W2899285308 cites W2100582962 @default.
• W2899285308 cites W2108875018 @default.
• W2899285308 cites W2112796928 @default.
• W2899285308 cites W2114691161 @default.
• W2899285308 cites W2117513900 @default.
• W2899285308 cites W2119412782 @default.
• W2899285308 cites W2123441931 @default.
• W2899285308 cites W2140099276 @default.
• W2899285308 cites W2140883620 @default.
• W2899285308 cites W2144086069 @default.
• W2899285308 cites W2155507705 @default.
• W2899285308 cites W2159092541 @default.
• W2899285308 cites W2165547003 @default.
• W2899285308 cites W2166153887 @default.
• W2899285308 cites W2180404227 @default.
• W2899285308 cites W2205505631 @default.
• W2899285308 cites W2239090104 @default.
• W2899285308 cites W2264017649 @default.
• W2899285308 cites W2287984595 @default.
• W2899285308 cites W2292706259 @default.
• W2899285308 cites W2306179365 @default.
• W2899285308 cites W2336857745 @default.
• W2899285308 cites W2342299314 @default.
• W2899285308 cites W2345013090 @default.
• W2899285308 cites W2438154367 @default.
• W2899285308 cites W2462449071 @default.
• W2899285308 cites W2511229982 @default.
• W2899285308 cites W2530594920 @default.
• W2899285308 cites W2532091902 @default.
• W2899285308 cites W2563912688 @default.
• W2899285308 cites W2566386603 @default.
• W2899285308 cites W2573637561 @default.
• W2899285308 cites W2576005654 @default.
• W2899285308 cites W2600626962 @default.
• W2899285308 cites W2601926225 @default.
• W2899285308 cites W2604637348 @default.
• W2899285308 cites W2609814551 @default.
• W2899285308 cites W2754499625 @default.
• W2899285308 cites W2762830435 @default.
• W2899285308 cites W2765134354 @default.
• W2899285308 cites W2767060943 @default.
• W2899285308 cites W2767731367 @default.
• W2899285308 cites W2769630486 @default.
• W2899285308 cites W2770747479 @default.
• W2899285308 cites W2799487719 @default.
• W2899285308 cites W2804672687 @default.
• W2899285308 cites W2888375528 @default.
• W2899285308 cites W2905060314 @default.
• W2899285308 cites W2915975108 @default.
• W2899285308 cites W4229732967 @default.
• W2899285308 cites W4249273120 @default.
• W2899285308 cites W776567260 @default.
• W2899285308 doi "https://doi.org/10.1101/461673" @default.
• W2899285308 hasPublicationYear "2018" @default.
• W2899285308 type Work @default.
• W2899285308 sameAs 2899285308 @default.
• W2899285308 citedByCount "0" @default.
• W2899285308 crossrefType "posted-content" @default.
• W2899285308 hasAuthorship W2899285308A5009026385 @default.
• W2899285308 hasAuthorship W2899285308A5043640443 @default.
• W2899285308 hasAuthorship W2899285308A5057312938 @default.
• W2899285308 hasAuthorship W2899285308A5060017240 @default.
• W2899285308 hasAuthorship W2899285308A5061712236 @default.
• W2899285308 hasAuthorship W2899285308A5062986831 @default.
• W2899285308 hasAuthorship W2899285308A5086402061 @default.
• W2899285308 hasBestOaLocation W28992853081 @default.

• next