htbc.stanford.edu
Compound Libraries for HTS - High-Throughput Bioscience Center (HTBC) - Stanford University School of Medicine
http://htbc.stanford.edu/compounds.html
High-Throughput Bioscience Center (HTBC) –. Compound Libraries Available for High-Throughput Screening (HTS) in the Stanford HTBC. Our compound library contains over 130,000 diverse compounds from ChemDiv. 30K), and Chembridge. 235K) Since our emphasis is on diversity, most of our compounds are not from combinatorial nor directed libraries, although we do have 10,000 combinatorial compounds from ChemRX and 10,000 compounds from the Kinase directed set from ChemDiv ( Mitotic Kinase targeted library info.
imdevsoftware.wordpress.com
Enrichment Network | Creative Data Solutions
https://imdevsoftware.wordpress.com/2014/05/11/enrichment-network
When you want to get to know and love your data. Enrichment is beyond random occurrence within a category. Networks can represent relationships among variables. Enrichment networks display relationships among variables which are over represented compared to random chance. Next is a tutorial for making enrichment networks for biological (metabolomic) data in R using the KEGG database. This entry was posted on May 11, 2014 by dgrapov. It was filed under Uncategorized. And was tagged with biochemical network.
bioclipse.net
Planet Bioclipse | Bioclipse
http://bioclipse.net/aggregator/sources/1
Http:/ www.example.com/. 45 min 5 sec ago. Chem-bla-ics : Comparing sets of identifiers: the Bioclipse implementation. Sat, 2016-05-21 11:22. All metabolites from WikiPathways. This set has many different data sources, and seven provide more than 100 unique identifiers. The full list of metabolite identifiers is here. Determine the interaction of two collections of identifiers from arbitrary databases, ultimately using scientific lenses. I will develop at least two solutions: one based on Bioclipse.
pele.farmbio.uu.se
Planet Bioclipse
http://pele.farmbio.uu.se/planetbioclipse
Comparing sets of identifiers: the Bioclipse implementation. That sounds easy: take two collection of identifiers, put them in sets, determine the intersection, done. Sadly, each collection uses identifiers from different databases. Worse, within one set identifiers from multiple databases. Mind you, I'm not going full monty, though some chemistry will be involved at some point. Instead, this post is really based on identifiers. All metabolites from WikiPathways. This post) and one based on R (later).
