Just read the paper (doi:10.15252/msb.202110387).

Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling.

Kat Day wrote this summer about nonsensical nomenclature: "The name wasn’t in my dictionary, nor the index of the few expensive textbooks I’d scraped together the funds to buy". Day is talking here about capric acid (GHVNFZFCNZKVNT-UHFFFAOYSA-N).

That sounds so familiar: both looking up facts, and collecting some second hand books. I found myself in the same situation in 1993-1994 when I started my chemistry degree.

I have been funded to answer biological questions around the safety of nanomaterials. In particular, to what extend can we understand and therefore predict the safety of new materials, often referred to as safe-by-design. We started with eNanoMapper and continued with NanoCommons, NanoSolveIT, RiskGONE, and SbD4Nano, and supervising a PhD student working on the EU-ToxRisk and OpenRiskNet projects.

Omics analysis gives results for many entities at the same time. Pathways databases describe the interactions of those entities. Combining this gives a lot of statements about that state of the biological system, but without filtering looks like a hairbal, all in an attempt to see the forest for the trees. But the forest is dense, large, and full of ever moving ents. Over the years many approaches have been invented to help us see the paths through the hairball.
Text
Text
This blog deals with chemblaics in the broader sense. Chemblaics (pronounced chem-bla-ics) is the science that uses computers to solve problems in chemistry, biochemistry and related fields. The big difference between chemblaics and areas such as chem(o)?informatics, chemometrics, computational chemistry, etc, is that chemblaics only uses open source software, open data, and open standards, making experimental results reproducible and validatable. And this is a big difference!
About Me
About Me
Popular Posts
Popular Posts
Pageviews past week
Pageviews past week
1831
Blog Archive
Blog Archive
Labels
Labels
Loading
Dynamic Views theme. Powered by Blogger. Report Abuse.