Sunday Reads – Links I Found Interesting 6/9

Mapping human microbiome drug metabolism by gut bacteria and their genes

A fascinating look at how the microbiome may affect drug metabolism. Important to remember that the game does not end at pharmacogenomics and we need to be paying attention to the complex interplay of numerous complex systems to understand drug action.

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Thanks to the ‘magic’ of deep learning we may be able to better predict which patients are going to respond to immunotherapy in gastrointestinal cancer with cheaper tests. More people treating their cancer certainly sounds good to me.

CCR5-∆32 is deleterious in the homozygous state in humans

The gene that was CRISPR-ed in those Chinese babies makes it more likely you die. This isn’t even accounting for the potential off-target effects. Turns out the thing we all knew was unethical is in fact unethical. Who da thunk?

Principles of and strategies for germline gene therapy

Following in the same vein as the previous article we take a theoretical look at the potential for these germline therapies.

The Sweetgreenification of Society

Interesting Substack post about the increasing stratification of society through the lens of boutique businesses.

RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues

From one, many. Our bodies are a huge mess of different mutations each of which could or could not be maybe contributing to diseases. Thinking of yourself as having one genetic identity is flawed.

A Jaunt Down Financial Fraud Lane

A fun article taking a look at some of the numerous scams in the cryptocurrency ecosystem. I am partial to the disaster that is EOS.

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Sunday Reads – Links I Found Interesting 6/2

EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling

Google did that thing they do again where they make vast steps in artificial intelligence and machine learning. Efficiency of these image recognition networks is up to 10* greater. Most of this gain is because they try to use “scaling coefficients” so that the network scales in a predictable way. I’m just mad because it’s a TensorFlow model and not PyTorch so I can’t drop it into any of my existing image recognition notebooks.

A promising step forward for predicting lung cancer

Another Google blog post about how they are doing incredible things. Man what I wouldn’t do to work for Google Brain. (This research is also being done at the same university I am doing my capstone with, so hey maybe they can sneak me in) Okay so in this article they describe a state of the art result for predicting lung cancer using improved volumetric predictions of CT scans. They instead of looking at individual slices in the image are instead reconstructing 3-d structures to improve the accuracy. This both is and is not a crazy step forward. Being able to use the 3-d structure seems to be truly revolutionary, but some of the radiologists performed equally as well as it. Seems that it will be a useful assistance tool for now.

Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

I promise this won’t be all Google, but again what they are doing right here is incredibly cool and a little bit scary. They have found a way to approximate the 3-d size, shape, and depth of moving people even when the camera is moving. This work has really cool implications for AR and VR and a little bit terrifying uses for a potential police state. There are many places where face recognition has been banned or people are considering banning it, however, combining a 3-d map of a person with existing effective identification techniques like gait tracking can serve as a proxy for facial recognition in those areas. Combined with facial recognition it could provide an even stronger match limiting false positives, and avoiding false negatives.

Speech2Face: Learning the Face Behind a Voice

Okay we are finally away from Google, but into something even more terrifying. This neural network when fed a small sample of speech is able to generate a qualitatively accurate facial guess. The model seems quite adept at identifying both race and gender. Scary stuff.

Defund Crypto

This fun parody site created by Joshua Davis, Kyle Gibson, and the pseudonymous Cas Piancey mercilessly lampoons the tomfoolery of Kik’s attempt to challenge the SEC. For the record, I do not think promising an Ethereum public DApp and delivering a one node Stellar fork is a good thing.

Exist 

This interesting service will likely not be appreciated by the privacy minded. While they do have a strong privacy policy its purpose is to bring a ton of your disparate personal data together and find interesting correlations. Whether it is useful or just noise remains to be seen.

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