chem-bla-ics: November 2015

Databasing nanomaterials: substance APIs

Nanomaterials are quite interesting from a science perspective: first, they are materials and not so well-defined as such. The can best be described as a distribution of similar nanoparticles. That is, unlike small compounds, which we commonly describe as pure materials. Nanomaterials have a size distribution, surface differences, etc. But akin the QSAR paradigm, because they are similar enough, we can expect similar interaction effects, and thus treat them as the same. A nanomaterials is basically a large collection of similar nanoparticles.

Until the start interacting, of course. Cell membrane penetration is studies at a single nanoparticle level, and they make interesting pictures of that (see top left). Or when we do computation. Then too, we typically study a single materials.

Nov

Twitter at conferences

I have been happily tweeting the BioMedBridges meeting in Hinxton last week using the #lifesciencedata hashtag, along with more than 100 others, though a small subset was really active. A lot has been published about using Twitter at conference, like the recent paper by Ekins et al (doi:10.1371/journal.pcbi.1003789).

The backchannel discussions only get better when more and more people join, and when complementary information is passed around.

Nov

Got access to literature?

Got access to literature? Only yesterday I discovered that resolving some Nature Publishing Group DOIs do not necessarily lead to useful information. High quality metadata about literature is critical for the future of science. Elsevier just showed how creative publishers can be in interpreting laws and licenses (doi:10.1038/527413f).

So, it may be interesting to regularly check your machine readable Open Access metadata. ImpactStory helps here with their Open Access Badge.

Nov

WikiPathways: capturing the full diversity of pathway knowledge

Biology is a complex matter. The biological matter indeed involves many different chemicals in very many temporospatial forms: small compounds may be present in different charge states (proteins too, of course), tautomers, etc. Proteins may exhibit isoforms, various post-translational modifications, etc. Genes shows structures we are only now starting to see: the complex structures in the nucleus have been invisible to mankind until some time ago.

Nov

RRegrs: exploring the space of possible regression models

Machine learning is a field of science that focusses on mathematically describing patterns in data. Chemometrics does this for chemical data. Examples are (nano)QSAR where structural information is related to biological activity. I studied during my PhD studies the interaction between the statistics and machine learning with how you computationally (numerically) represent the question.