The AI community must collaborate with geneticists, in finding a treatment for those deemed most at risk of coronavirus. A potential treatment could involve removing a person’s cells, editing the DNA and then injecting the cells back in, now hopefully armed with a successful immune response. This is currently being worked on for some other vaccines.
The first step would be sequencing the entire human genome from a sizeable segment of the human population.
Sequencing Human Genomes
Sequencing the first human genome cost $2.7 billion and took nearly 15 years to complete. The current cost of sequencing an entire human has dropped dramatically. As recent as 2015 the cost was $4000, now the cost is less than $1000 per person. This cost could drop a few percentage points more when economies of scale are taken into consideration.
We need to sequence the genome of two different types of patients:
- Infected with Coronavirus; but healthy
- Infected with Coronavirus; but poor immune response
It is impossible to predict which data point will be most valuable, but each sequenced genome would provide a dataset. The more data the more options there are to locate DNA variations which increase a body’s resistance to the disease vector.
Nations are currently losing trillions of dollars to this outbreak, the cost of $1000 a human genome is minor in comparison. A minimum of 1,000 volunteers for both segments of the population would arm researchers with significant volumes of big data. Should the trial increase in size by one order of magnitude, the AI would have even more training data which would increase the odds of success by several orders of magnitude. The more data the better, which is why a target of 10,000 volunteers should be aimed for.
While multiple functionalities of machine learning would be present, deep learning would be used to find patterns in the data. For instance, there might be an observation that certain DNA variables correspond to a high immunity, while others correspond to a high mortality. At a minimum we would learn which segments of the human population are more susceptible and should be quarantined.
To decipher this data an Artificial Neural Network (ANN) would be located on the cloud, and sequenced human genomes from around the world would be uploaded. With time being of the essence, parallel computing will reduce the time required for the ANN to work its magic.
We could even take it one step further and use the output data sorted by the ANN, and feed it into a separate system called a Recurrent Neural Network (RNN). The RNN uses reinforcement learning to identify which gene selected by the initial ANN is most successful in a simulated environment. The reinforcement learning agent would gamify the entire process of creating a simulated setting, to test which DNA changes are more effective.
A simulated environment is like a virtual game environment, something many AI companies are well positioned to take advantage of based on their previous success in designing AI algorithms to win at esports. This includes companies such DeepMind and OpenAI.
These companies can use their underlying architecture optimized at mastering video games, to create a stimulated environment, test gene edits, and learn which edits lead to specific desired changes.
Once a gene is identified, another technology is used to make the edits.
Recently, the first ever study using CRISPR to edit DNA inside the human body was approved. This was to treat a rare type of genetic disorder that effects one of every 100,000 newborns. The condition can be caused by mutations in as many as 14 genes that play a role in the growth and operation of the retina. In this case, CRISPR sets out to carefully target DNA and to cause slight temporary damage to the DNA strand, causing the cell to repair itself. It is this restorative healing process which has the potential to restore eyesight.
While we are still waiting for results on if this treatment will work, the precedent of having CRISPR approved for trials in the human body is transformational. Potential disorders which can be treated include improving a body’s immune response to specific disease vectors.
Potentially, we can manipulate the body’s natural genetic resistance to a specific disease. The diseases that could potentially be targeted are diverse, but the community should be focusing on the treatment of the new global epidemic coronavirus. A threat that if unchecked could lead to a death sentence to a large percentage of our population.
While there are many potential options to achieving success, it will require that geneticists, epidemiologists, and machine learning specialists unify. A potential treatment option may be as described above, or may be revealed to be unimaginably different, the opportunity lies in the genome sequencing of a large segment of the population.
Deep learning is the best analysis tool that humans have ever created; we need to at a minimum attempt to use it to create a vaccine.
When we take into consideration what is currently at risk with this current epidemic, these three scientific communities need to come together to work on a cure.