Robot | Path | Permission |
GoogleBot | / | ✔ |
BingBot | / | ✔ |
BaiduSpider | / | ✔ |
YandexBot | / | ✔ |
User-agent: * |
Title | BayesPrism |
Description | Gateway BayesPrism Gateway Home BayesPrism doc Software/Package FAQ About Tuorial Deconvolution Create Account Login --> Toggle navigation PGA --> Create ac |
Keywords | N/A |
WebSite | bayesprism.org |
Host IP | 149.165.155.90 |
Location | United States |
Site | Rank |
US$1,365
Last updated: 2023-05-20 08:13:02
bayesprism.org has Semrush global rank of 0. bayesprism.org has an estimated worth of US$ 1,365, based on its estimated Ads revenue. bayesprism.org receives approximately 157 unique visitors each day. Its web server is located in United States, with IP address 149.165.155.90. According to SiteAdvisor, bayesprism.org is safe to visit. |
Purchase/Sale Value | US$1,365 |
Daily Ads Revenue | US$1 |
Monthly Ads Revenue | US$37 |
Yearly Ads Revenue | US$453 |
Daily Unique Visitors | 10 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
bayesprism.org. | A | 1798 | IP: 149.165.155.90 |
bayesprism.org. | NS | 1800 | NS Record: dns2.registrar-servers.com. |
bayesprism.org. | NS | 1800 | NS Record: dns1.registrar-servers.com. |
bayesprism.org. | MX | 1800 | MX Record: 10 eforward2.registrar-servers.com. |
bayesprism.org. | MX | 1800 | MX Record: 10 eforward3.registrar-servers.com. |
bayesprism.org. | MX | 1800 | MX Record: 20 eforward5.registrar-servers.com. |
bayesprism.org. | MX | 1800 | MX Record: 15 eforward4.registrar-servers.com. |
bayesprism.org. | MX | 1800 | MX Record: 10 eforward1.registrar-servers.com. |
bayesprism.org. | TXT | 1800 | TXT Record: v=spf1 include:spf.efwd.registrar-servers.com ~all |
BayesPrism Gateway Home BayesPrism doc Software/Package FAQ About Tuorial Deconvolution Create Account Login --> Toggle navigation PGA --> Create account Log in Data explorer: scRNA-seq of mouse small and large intestine tissue BayesPrism Service BayesPrism is a fully Bayesian inference of tumor microenvironment composition and gene expression. It consists of the deconvolution module and the embedding learning module. The deconvolution module leverages cell-type-specific expression profiles from scRNA-seq and implements a fully Bayesian inference to jointly estimate the posterior distribution of cell-type composition and cell type-specific gene expression from bulk RNA-seq expression of tumor samples. The embedding learning module uses Expectation-maximization (EM) to approximate the tumor expression using a linear combination of tumor pathways while conditional on the inferred expression and fraction of non-tumor cells estimated by the deconvolution module. Only the |
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