Personalizing Healthcare Through Data With General Genomics

Published on May 21, 2021 by Ditsa Keren – DNA Weekly (

General Genomics is a bioinformatics artificial intelligence platform that provides customers with a comprehensive analysis of disease susceptibility and treatment effectiveness. In this interview, co-founders AJ Rosenthal and Warren Gieck discuss the importance of medical data ownership and its anticipated impact on personalized and preventative healthcare.

Please describe the story behind the company: What sparked the idea, and how has it evolved so far?

AJ: We started General Genomics right as the pandemic kicked off, around March of 2020. Schools were shut down, so I had my son with me, and he was asking some questions about Coronavirus and whether or not we would get sick or die. He was clearly upset and frightened, so I promised I would figure it out and called Warren, my old colleague from General Electric.

At the time, Warren was working in the artificial intelligence sphere at the center of innovation in Calgary, Alberta, where we first met. I called him up and told him about my idea, which seemed crazy at the time, to apply machine learning algorithms and put them into something that would solve the world’s problems. Surprisingly, we came to realize that no one has done it before. We had a scratch-your-head moment, and the next thing we knew it, we were getting these patents registered and starting our journey as a company.

Warren: AJ and I have known each other for quite a few years. We used to work at General Electric together. I led artificial intelligence and machine learning projects across different industries and verticals, including aviation, oil and gas, and healthcare.

In aviation, they process a terabyte of data every time a plane comes off the ground. We are used to processing vast amounts of data. We were able to work through DNA and medical datasets with surprising ease.

AJ: We are thought disruptors and innovators. We’re bringing ideas to market that nobody’s ever done before. Many people in the healthcare industry thought it was an impossible task because we weren’t directly from the healthcare industry. That actually gave us an advantage, both in terms of experience and know-how and in implementing solutions in a highly regulated industry that is swarmed with lobbyists and intermediaries with conflicting interests.

Because we come from an outside industry and expertise, our perspective is generally more holistic. We want a better place for our children and grandchildren to grow up in. That’s what General Genomics is all about.

What is the role of AI in healthcare?

Warren: Currently, the healthcare industry does not utilize even a fraction of their data. When you do a study, you’re intentionally only looking at one or two variables. For example, clinical studies will have a control group taking a placebo and a treatment group taking the actual drug. Even if you have thousands of participants, you’re not getting an accurate indication of why some people have side effects and don’t. The drug companies warn us that these side effects may occur, but there’s no correlation to why they would occur and who should be avoiding them. That’s essentially the challenge that we’re pursuing.

There’s a lot of data available now between genetics, wearable devices, and health analytics. People record so much data about their health and wellness. Somewhere in that data are the answers to those questions.

COVID is an excellent example where some people have severe or long-term reactions, while others show no symptoms at all. The potential correlations could be medications and supplements, or they could be age, race, and sex. When you bring machine learning into outlying areas of health care, such as mapping the genome, you begin to reveal the complex multi-dimensional relationships that determine one’s susceptibility to diseases and the effectiveness of treatments on their body.

All of those factors are important, but when you’re just looking at one or two variables, you only get a tiny piece of the potential correlation. For example, I have high blood pressure. Doctors call it Primary Hypertension, which means they have no idea what’s causing it. They’ve tried all sorts of drugs, and some of them actually increased my blood pressure, and they still don’t know why I have it.

The answer is somewhere in the data. It may be that we’re not collecting the right data at this point; maybe it’s not about genetics and is more to do with lifestyle. As we add more and more data, we’re able to get a clearer understanding and hopefully provide better treatments to individuals.

I think data needs to play a bigger role in healthcare, and we even hear that from doctors. We are working with a very influential Doctor who specializes in head trauma. One of his biggest concerns right now is the opioid crisis.

When a doctor like him has to prescribe painkillers, because their patient had a major accident, they don’t usually know which drugs would work best for that specific patient. They start with one drug, and if it doesn’t work, they try the next one, and the next one, and so on. During this trial and error, the prescribing doctor just hopes that it doesn’t harm them or get them addicted. Thankfully, we’re starting to see an increased use of data across the industry, and General Genomics is certainly at the forefront of that.

AJ: We have two registered patents that we filed in April of 2020. One is around susceptibility to disease and the other is about survivability from both disease and treatments.

Our susceptibility algorithm applies to all living organisms on the planet, humans and animals alike. That rolls into swine flu, avian flu, hoof and mouth disease, cattle string, etc.

Our second patent uses genetic data and predictive analytics to determine the degree of survivability with different treatment strategies.

We recently gave a talk to a group of doctors. They understood how artificial intellegence and machine learning would help them diagnose and prescribe medication, but they couldn’t understand the mechanism because the way they are taught in school is a lot more qualitative than quantitative. It’s a yes/no mechanism. Did the treatment work or not? Were there side effects or not?

When you start mapping it out genetically and environmentally, person by person, you see that even though we’re all made of the same building blocks, each one of us is unique. Therefore it makes sense that certain treatments will work for us and others won’t.

But General Genomics isn’t just about diseases. We know that people who live in certain environments are exposed to environmental contaminants. People who live by a power plant or large electrical wires might be faced with increased electromagnetic radiation. Over time, their body may become susceptible to it.

All those correlating factors produce predictive models. Our engineer-style approach to medicine breaks down the body into parts, just like an electromechanical system. If your heart is a pump and the synapses in your nervous system is the electrical system, your body is essentially a biomechanical system. With the right data, we can optimize our health at much greater efficiency than ever before.

General Genomics Interview

What do you think is required to make personalized health data more available to the general public?

As data becomes more and more available, doctors, like engineers, are searching for more scientific approaches that are backed by confirmed scientific data so they have better confidence in the treatment plans they offer to patients. This is where solutions like ours come into play. We’re certainly seeing that as we enable more of these tools, doctors themselves also change some of their strategies.

One of our driving forces is to give individuals the ability to control their own health and medical information. Right now, you typically don’t get to see your test results. If you do keep a copy of them, you might see a summary sheet, but you won’t get a copy of the raw data. That is your data!

You might be moving between states, provinces, and countries, or you might want to give your data to children, a research institute, or a different medical practice. Those are all examples highlighting the necessity to transfer medical data.

Since it’s your personal data, we believe you should be in control of it, but typically, only another doctor or physician can get the data. A lot of dentists nowadays are looking for things like your latest blood panels, scans, or information, but they don’t have access to those medical systems so they have to request it through the patient.

Individuals need to take more responsibility and ownership of their health and health data. Our database is fully anonymized, so even if somebody were to break the encryption levels, they wouldn’t be able to connect the person and the data. With that kind of security control, people could have exclusive access to their data and decide who they want to share it with.

How do you envision the future of healthcare?

Warren: I think the future of healthcare lies in solutions that will help both doctors and patients get a more comprehensive picture of potential side effects in response to a treatment or an illness, so they can make better, more informed health decisions.

Personalized medicine and telemedicine are becoming increasingly prevalent. Making that knowledge more available to professionals and individual patients is where healthcare needs to go.

Most people aren’t aware that healthcare is the number one expense in almost any government’s budget, outweighing education, military, and social welfare. Optimizing our health care system to be more efficient with data could greatly impact our economies. It’s not just about the health of individuals, but about public health as a whole, both physical and mental.

We are seeing more holistic approaches with a lot of practices in Canada and the US. More and more clinics are starting to have a variety of medical and health resources. Certainly, alternative medicines and healers are gaining recognition and more and more people are using them, searching for more personalized and preventative medicine.

Functional medicine has increased dramatically. Supplements, vitamins, and preventative diets have been trending. For example, during the pandemic, there has been a lot of information on the importance of zinc, vitamin D, and other preventative supplements. Doctors are certainly not ignoring it, but change takes time. I think it’s going to trend more and more that way but it’s really up to the individual to take control of their own health and wellness. It’s to do with the patient choosing their own path or helping to educate their doctors

Of course, there are certain areas where hospitals and doctors don’t necessarily need personalization, like trauma, broken bones, accidents, or heart attacks. So I think the change has to happen at the family practitioner level.

I look at the pandemic and the decisions that go back and forth every day. They have just as many doctors on one side as they do on the other; they cannot reach a consensus, and the discussions around it have become almost political. From our perspective, if you remove the interpretations out of the equation, you’ve already done a lot. At General Genomics, we take an agnostic, engineerial approach. We’re not here to choose sides. We just want to provide doctors with better tools so they can utilize data more effectively.  Solutions like ours help reveal that nothing is absolute, and that nature is wild and far more complex than we think. Using AI, we can create the tools for a new type of medicine that is far more accurate than we ever imagined.

Viral Risk Assessment Through Deep Machine Learning of Genetic Testing

Daniel A. Brue, PhD


In this document, we outline the goals and purpose of General Genomics, and its methodology for providing metrics of viral disease propagation and individual susceptibility and response. We have developed a process to quantitatively define an individual’s risk during the COVID-19 pandemic, though the methods apply equally for other diseases as well. In this way, we are able to provide insights into disease propagation, population susceptibility, and personal risk to infection. With this information, we will help businesses, governments, and individuals to make better choices regarding public and personal safety. 



General Genomics LLC is an endeavor to answer some of the questions regarding virus susceptibility, spread, and individual response to illnesses. The COVID-19 global pandemic is currently still in effect and likely will continue through several more months. This provides both a strong motivation and an unprecedented opportunity in studying viras dispersion and human reaction to specific virus infection. At no time in history have we had more information with which to work. Indeed, the most common comparison we use is the influenza outbreak of 1918. Today, modern technology provides a far better understanding of how COVID-19 has spread across the world and far more accurate medical tools for detecting and treating the disease. 

General Genomics has developed a process for using available data to provide risk assessments. This result is called a Risk Under Normalcy (RUN) score. 

The RUN score is a metric that gives a quantitative measure of individual risk of disease susceptibility. The RUN score is a result of multivariate factors that includes testing for genetic markers that may make one more or less inclined to infections. It has been shown that there do 2 exist genetic predispositions that affect one’s susceptibility and resilience to COVID-19 as well as other diseases. 

Data Collection 

General Genomics has developed a survey and an application interface that allows individuals or medical institutions to provide a person’s genetic information and demographic factors. Using either the app available on Google Play or the Apple Store, or through, anyone can upload their genetic information and any other factors they might supply, and they will receive a RUN score with a report of their most significant risk factors. 

Data Management 

All data collected will be stored and managed according to HIPAA compliance and user license agreements. Only data in aggregate will be shared or used for analysis, and individual identification will not be used for tracking. 

Several methodologies are available for managing COVID-19 data 3 . Initially, a simple queried database will be sufficient, but will transfer into a more reliable cloud system as need arises. 



Some work has been published in tracking propagation of COVID-19 based on statistical inference 45 including Johns-Hopkins6 , the CDC7 , research has shown that certain genetic markers are correlated with COVID0-19. With sufficient data, we will be able to confirm and/or refine these conclusions and will publish our findings. 

Many options already exist for machine learning and artificial intelligence (ML/AI) codes, and the methodology deployed by General Genomics will be chosen based on available data types and the specific questions to be answered. The chosen methods will be compared, weighted, and tuned based on empirical field data. 


Initial analysis shows which factors are most significant in answering the following questions: 
1. Which genetic markers are most significant to an individual’s risk of contracting COVID-19?
2. Which environmental factors, including workplace exposure, family interaction, and general exposure to the public, are most influential in a person’s general risk?
3. What independent factors, such as smoking, prescription medication, etc. should be considered in diagnosis and treatment plans?
4. Can we increase the accuracy of models tracking and predicting the spread of the pandemic by having a much more accurate understanding of human response and resilience? 

By identifying significant factors, the resultant RUN score is far more than just a number, but allows a person to weigh their own risks and take mitigating measures to reduce their risk. For example, we can provide a list of the most significant factors adding to someone’s risk, thereby allowing the person to make better informed choices for self protection and care. 

Expected Results: 

1. Inform an individual and their medical care provider information on the individual’s risk and primary risk factors. 


2. Inform businesses that track RUN scores on their aggregate risk by allowing them to set policy on high or low risk customers, especially in situations of high population density and personal interaction.
3. Provide data on similar cases and which treatments have been most effective in combating the disease.
4. An assessment of how the disease spreads, including factors, but not limited to, social distancing and isolation. 

Based on results, the model will be continuously updated and refined. As new factors present themselves, we will be able to develop improved products to better inform the population, business, and the scientific medical community of the results. We will also be able to provide increasingly accurate products and services.

Edmond Sun: Investigator unraveling mystery of COVID-19 genetic markers and virus susceptibility

Local genome researcher Daniel Brue investigates why some people are more susceptible to COVID-19 while others are not. As an inventor and the founder of General Genomics, he has established a group of people in an attempt to find more information and correlations between genetic markers and virus susceptibility of COVID-19.

Investigator unraveling mystery of COVID-19 genetic markers and virus susceptibility

The findings could potentially reveal effective methods of treatment against the virus.

“What we do know now is that there is a significant part of the population A-symptomatic to COVID-19,” said Brue, P.h.D. “So they are carriers, but they don’t know that they’re ill.”

Brue is part of a group whose focus is to increase the effectiveness and preventiveness of treatments and illnesses by warning people to understand what they may be susceptible to, based on their genetic information.

Brue said a large population of participants in companies such as 23andMe and have been receiving reports about their genetic information.

“What I would like to track is how a disease effects people of different genetic dispositions,” Brue said.

A clearer picture of genetic markers linked to disease is forming from incoming information and volunteer participants. Brue correlates the effectiveness of treatments participants have received based on their genetic bands.

COVID-19 is becoming one of the best documented cases of a pandemic, and it is Brue’s hope that the group’s findings will apply to a bigger picture, triggering further scientific research of other disease processes as well.

“What I would want people to know is we have greater capacity to understand what is happening than we have ever had before,” Brue said. “If we didn’t take advantage of learning as much as we possibly can, we would be horribly remiss in not using data that we have on hand to try to improve people’s health care, and understand on the onset, what is the most effective treatment for those who are ill.”

The three inventors of the new program combine expertise in several disciplines. Ultimately they want to save lives.

Brue has an extensive background in physics and artificial intelligence/machine learning, and medical image processing. He earned his doctorate at the University of Oklahoma. Brue said he understands how sensors work and how to get the best information from them.

“What I know very well is how to extract information from measuring apparatuses that we’re using,” he said. 

Warren Gieck, of Calgary, Alberta, is an entrepreneur and industrial engineer, with experience in software development, artificial intelligence, robotics, mechatronics, and product development. 

“Our motivation is the suffering of our friends and society around us. And just as importantly, we are dads whose kids just want to go back to school,” Gieck said. “With extensive scientific and engineering expertise, we have built solutions using similar technologies for industrial applications, and we saw how we could help solve the uncertainty around the Covid-19 virus.

“Ultimately our goal is to allow people who are low risk to get back to their lives.”

A.J. Rosenthal of Midland, Texas, has a background in multi-disciplinary engineering solutions, nuclear engineering technology, and finance. Kyrie Cameron, attorney at Patterson + Sheridan, has assisted these inventors in filing their patent applications.   

“I want to figure out a way that we can better identify what people should be looking for in their own health care,” Brue said.

The goal is provide people a better understanding of how to take care of their personal health. By understanding individual risks, individuals would be able to provide care providers a better understanding of how they should be treated should they be in poor health, Brue said. As a result, physicians would have more concrete information to work with in patient care.

Brue said one of the worst aspects of what anyone goes through when they become sick is their uncertainty. A lot of people are concerned and scared of COVID-19.

“I have lived through enough personal losses to see how much the damage is on not just the person who’s ill, but their entire family around them,” Brue said.

His goal is to reduce anxiety by educating people about disease processes.

“It’s personally important to me,” Brue said.

Houston Chronicle: Midlander creates algorithm to predict likelihood of infection

Determination would be made using person’s genetic make-up, medical history

By Caitlin Randle, Reporter-Telegram Published 9:11 pm CDT, Thursday, April 16, 2020

A Midland data scientist and his two partners have created an algorithm that uses a person’s genetic markers and medical history to predict someone’s likelihood of becoming infected with the coronavirus and suffering complications from it.

Midlander A.J. Rosenthal and his partners, Dan Brue of Oklahoma and Warren Gieck of Alberta, Canada, filed patents this week related to the algorithm.

Rosenthal said it could use a person’s genetic make-up in combination with various factors, such as their medical history and types of exposure they’ve had (i.e. a miner exposed to coal dust), to determine someone’s risk factor and assign them a correlating score.

“We’re describing potentially where a person would fall, give them a score, and that score allows them to either start going back to the workplace because they’re not going to succumb to the disease, or they won’t even be susceptible to it,” he said.

The algorithm would use the medical histories of those who have been hospitalized with COVID-19 to determine what markers could put a person at risk, Rosenthal said. He described inputting the data from past patients as “training the algorithm.”

The goal of this project is for the information to be widely accessible, Rosenthal said. He said the algorithm could potentially be on a website where a person could enter their medical information after signing a HIPPA privacy release.

“What we’re trying to do is if people want this – and we’re hoping they do – is to make it easier for them to feel comfortable and safe going back out,” he said. “Because they’ve now been locked in their houses for weeks … they don’t know if they’re going to get sick. They don’t know if they’re even susceptible to it.”

The algorithm could also be applied to other viruses and diseases, Rosenthal said, but the trio has chosen to focus on COVID-19 because there’s an immediate need.

The project’s success is contingent on partnerships with other entities – primarily, with medical providers who would give access to the medical histories of past COVID-19 patients. HIPPA laws prevent that data from being publicly available.

Rosenthal pointed to studies linking ACE2 receptors in the lungs to COVID-19 as evidence that a person’s DNA could be used to predict their risk of being infected. Some studies have found the coronavirus uses these receptors to infiltrate cells in the body.

“When the coronavirus attaches, it has a certain type of envelope that it attaches to,” Rosenthal said. “Your receptor on your lung, a lot of the coronavirus sticks to it … and from there, it propagates an infection.”

Some health entities worldwide have advised against using ibuprofen to treat COVID-19 because it’s thought to increase the number of ACE2 receptors in the body, but there’s no clear consensus among the scientific community about whether more of these receptors create a higher risk of contracting or having complications from the coronavirus.

Rosenthal said the algorithm could determine if certain combinations of medications and genetics were frequently present in those infected with the virus and serve as a guide to those with similar DNA who are also on those medications.

A former multi-disciplinary engineer in the U.S. Navy and at General Electric, Rosenthal currently works for an oil and gas company in Midland. He said he and his partners, who met working at GE, were inspired to take up this enterprise by their kids, who want to “go back to school and go to the mall and play baseball.”

“We’re just three dads. We just want our kids to have a normal life again,” Rosenthal said.

“Maybe these three dads can help the world,” he said. “The only thing we’ve got left to lose are our jobs or the economy.”