A previous OPIGlet discussed biscuits in his blog post.
Not to be outdone, I will now discuss something even more nerdy: new avenues for comic book adapations. For brevity’s sake, this OPIGlet will focus on the DC and Marvel Corporations as key examples.1
Monthly Archives: November 2019
Journal Club: Is our data biased, and should it be?
Last week I presented the above paper at group meeting. While a little different from a typical OPIG journal club paper, the data we have access to almost certainly suffers from the same range of (possible) biases explored in this paper.
Continue readingIEEEGor Knows What You’re Thinking…
Last month, I, EEGor, took part in the Brain-Computer Interface Designers Hackathon (BR41N.IO), the opening event of the IEEE Systems, Man and Cybernetics Conference in Bari, Italy. Brain-Computer Interfaces (BCIs) are a class of technologies designed to translate brain activity into machine actions to assist (currently in clinical trials) as well as (one day) enhance human beings. BCIs are receiving more and more media attention, most recently with the launch of Elon Musk’s newest company, Neuralink which aims to set up a two-way communication channel between man and machine using a tiny chip embedded in the brain. With the further aim of one-day perhaps making our wildest transhumanist dreams come true…
Continue readingConsistent plotting with ggplot
Unlike other OPIGlets (looking at you, Claire), I have neither the skill nor the patience to make good figures from scratch. And making good figures — as well as remaking, rescaling and adapting them — is incredibly important, because they play a huge role in the way we communicate our research. So how does an aesthetically impaired DPhil student do her plotting?
Continue readingHow to be a Bayesian – ft. a completely ridiculous example
Most of the stats we are exposed to in our formative years as statisticians are viewed through a frequentist lens. Bayesian methods are often viewed with scepticism, perhaps due in part to a lack of understanding over how to specify our prior distribution and perhaps due to uncertainty as to what we should do with the posterior once we’ve got it.
Continue readingde novo Small Molecule Design using Deep Learning
This is an interesting paper by Zhavoronkov, et al. that recently got published in Nature Biotechnology as a brief communication: https://www.nature.com/articles/s41587-019-0224-x. The paper describes a new deep generative model called generative tensorial reinforcement learning (GENTRL), which enables optimization for synthetic feasibility, novelty, and biological activity. In this work, authors have deigned, synthesized, and experimentally validated molecules targeting discoidin domain receptor 1 (DDR1) in less than two months. The code for GENTRL is available here: https://github.com/insilicomedicine/gentrl.
Reference: Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology 2019, 37, 1038-1040.
COSTNET19 Conference
Last month, I attended the COSTNET19 Conference in Bilbao (Spain). This conference is organised by COSTNET, a COST Action which aims to foster international European collaboration on the emerging field of statistics of network data science. COSTNET facilitates interaction and collaboration between diverse groups of statistical network modellers, establishing a large and vibrant interconnected and inclusive community of network scientists.
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