xinhelab.org valuation and analysis

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Title HE LAB | Translating Genomic Data to Knowledge of Human
Description HE LAB Translating Genomic Data to Knowledge of Human Diseases Menu Lab Research Publications People Join us News Lab Our lab is located in Department of
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WebSite xinhelab faviconxinhelab.org
Host IP 184.154.119.210
Location United States
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xinhelab.org Valuation
US$1,942,928
Last updated: 2023-05-06 06:53:52

xinhelab.org has Semrush global rank of 5,447,605. xinhelab.org has an estimated worth of US$ 1,942,928, based on its estimated Ads revenue. xinhelab.org receives approximately 224,184 unique visitors each day. Its web server is located in United States, with IP address 184.154.119.210. According to SiteAdvisor, xinhelab.org is safe to visit.

Traffic & Worth Estimates
Purchase/Sale Value US$1,942,928
Daily Ads Revenue US$1,794
Monthly Ads Revenue US$53,805
Yearly Ads Revenue US$645,650
Daily Unique Visitors 14,946
Note: All traffic and earnings values are estimates.
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Host Type TTL Data
xinhelab.org. A 14400 IP: 184.154.119.210
xinhelab.org. NS 86400 NS Record: chi-ns1.websitehostserver.net.
xinhelab.org. NS 86400 NS Record: chi-ns2.websitehostserver.net.
xinhelab.org. NS 86400 NS Record: ams-ns1.websitehostserver.net.
xinhelab.org. MX 14400 MX Record: 0 xinhelab.org.
HtmlToTextCheckTime:2023-05-06 06:53:52
HE LAB Translating Genomic Data to Knowledge of Human Diseases Menu Lab Research Publications People Join us News Lab Our lab is located in Department of Human Genetics at University of Chicago. We are broadly interested in understanding the genetic basis of complex human diseases. What are the genes and genetic variants that influence the susceptibility of diseases? What are the mechanisms linking genetic changes to phenotypic consequences? We develop and employ computational or statistical tools to address these challenges. Genomic technologies are generating a huge amount of data, exploring multiple dimensions of cellular processes such as transcriptome, epigenome and gene networks. These create great opportunities for computational analysis that extracts meaning from the data. We are particularly interested in developing novel methods that integrate multiple genomic datasets to have better power of detecting disease genes and gain deeper insights into the disease mechanisms. Some
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date: Tue, 26 Oct 2021 21:15:25 GMT
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