Using image recognition to make genetic predictions, Google scored the highest score in the FDA Challenge.
Last year, Google announced that it was moving from MobileFirst (first move to mobile) to AIFirst (artificial intelligence first). In the past few years, it has not only acquired a number of AI companies (including Metaweb, which focuses on semantic understanding and semantic networks; DNNresearch, a deep learning company; DeepMind, which focuses on machine learning and neural network research; chat bots API.AI, etc.) AlphaGo, developed by Google's DeepMind team, has also recently become a "net red", which last year had a gripping man-machine battle with Go World Champion and professional nine-player Li Shishi, with a total score of 4:1. Win. Google’s TensorFlow deep learning framework has also received wide acclaim.
Of course, Google has to continue to "do things" in the field of artificial intelligence.
Set goals beyond GATK
GATK (TheGenomeAnalysisToolkit) is the most widely used second-generation sequencing data analysis software, focusing on mutation search, genetic analysis and high attention to data quality. GATK uses a manual coding algorithm that can be applied to the most common errors in sequencing machines.
In June 2015, Google Genomics (Google Genome) announced a partnership with the BroadInstitute, which launched a cloud version of the genome analysis software GATK (TheGenomeAnalysisToolkit). The Google Genome is a cloud platform built by Google to help the scientific community store, process and manage massive amounts of genomic data, and was launched in March 2013. Scientists can use the API interface provided by Google Cloud to store, process, analyze and share DNA sequences.
However, Google is not satisfied with this and wants to develop an artificial intelligence-based tool that can surpass GATK. Previously, Google's artificial intelligence knew nothing about genetics. In fact, these artificial intelligence tools are only used to identify images, just as there are neural networks that recognize photos uploaded to Google Cats and Dogs. There are many places to learn.
Mark DePristo and Ryan Poplin took over the job and they were also members of the GATK creation team.
DePristo said: "(GATK) is no longer possible, we have built many different models, but it has no effect." Then, artificial intelligence technology appeared.
Just eight months later, the neural network (developed by Mark DePristo and Ryan Poplin) accurately determined the mutation points in the DNA sequence and won the highest score in the “FDA†Food and Drug Admistraton Challenge. In just one year, the artificial intelligence has surpassed the standard manual coding algorithm GATK, which is Google DeepVariant.
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