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To detect cancer we turned genomes into images and put them through DCNNs
- deleted 9y ago[deleted]
- urlwolf 9y agoHow come this is not bigger news?
- leblancfg 9y agoIt's the deep learning equivalent of a souped-up Honda Civic. Sure, with enough tweaking it'll eventually be competitive, but you could have just bought a racecar. Variant calling doesn't look like it needs to be turned into a image in the first place. You'd probably be better off feeding a regular, non-convolutional network some tabular data.
- malux85 9y agoThis is right, a convolutoonal network is the right choice for images, but images are not the right choice for “vectorising” the genomic data, because vectorising it in a 2D grid and then using a convolutional network on it (a network designed to exploit spatial hierarchy) is introducing unnecessary and arbitrary constraints on data locality. Definitely a souped up Honda Civic - love the metaphor
- 8iterations 9y agoTraditional variant calling, that is bayesian methods, have gotten very good at detecting point mutations, that is single letters. But have hit a wall when it comes to more complex structural mutations, we wanted to build this for example to focus on driver/passenger mutations in tumor/normal samples. (check out the "Preface" post for our LSTM version, and the logic behind this)
- 8iterations 9y agoHey, I'm one of the guys working on this. These are just our notes and drafts, and even though we are at ~97%-99.3% the goal is to get to 99.9587% or higher because out of 3-4 billion letters that is still significant. Here's a cool non-technical magazine article about it https://twitter.com/EricTopol/status/922315550054793216 https://twitter.com/EricTopol/status/922315550054793216