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It’s not easy even for experts to pull useful insights from complex datasets. It’s even harder when the machine learning tools they’re using are only trained to work within a single nation, region, or more specifically, racial population.
Consider the recent popular Netflix documentary Coded Bias. MIT Media Lab researcher Joy Buolamwini discovered that facial recognition does not see dark-skinned faces accurately. She’s pushed for legislation in the US to govern against bias in algorithms. It’s a real problem that many in healthcare are now facing as the use of artificial intelligence in health data analysis becomes more and more prevalent and clinically useful. But the same push for diversity in machine learning reference for health data has been lacking throughout the industry. For this reason, and for the reasons you’ll learn from just one case study below, SOPHiA GENETICS has taken a decentralized and global approach for more than a decade.
A family without answers
A Moroccan couple living in Spain had suffered one of the worst human experiences, not just once, but twice. As they began to expand their new family, they had three pregnancies throughout the span of about one decade. Only one of their children, a healthy baby girl born in 2012, survived. One boy lived for 43 days, and one girl lived only for 20. The immense grief that this brings to parents is immeasurable, and the only thing nearly as horrible than the actual experience of loss is having a lack of answers – never knowing why.
After the death of their infant boy in 2019, the couple worked with regional medical experts in Spain to try and discover the root cause of what seemed to be neurodevelopmental disorders at birth. Researchers started by performing clinical exome sequencing and targeted gene analysis including a Microarray-based Comparative Genomic Hybridization test to look for any abnormalities that would possibly provide more clues. Much to their dismay, the results were inconclusive. The standard of care and testing used at the time were letting them down.
In 2022, they were able to perform more advanced testing after the death of their second daughter. This included TRIO analysis with the SOPHiA DDM platform. TRIO analysis takes clinical exome testing to a deeper level by analyzing data from the infants as well as both parents, giving researchers a better picture of familial, inherited genomic traits. The results finally confirmed that both parents carried a heterozygous frameshift variant. It was inherited by their infants as a homozygous pathogenic variant in the SMPD4 gene, causing neurodevelopmental disorder with microcephaly, arthrogryposis, and structural brain anomalies in the children. While identifying causative variants in recessive genes is challenging through conventional clinical testing, the SOPHiA DDM™ platform’s TRIO analysis enabled the researchers to overcome this limitation.
This family is now better informed about their risk of potential complications in future pregnancies. They’re able to work with genetic counselors to seek guidance regarding family planning that takes the anxiety out of the unknown.
Why diversity matters
In this case, it wasn’t just the type of testing that resulted in a successful discovery. Because SOPHiA GENETICS has worked on a global scale since inception (unlike more common approaches), the SOPHiA DDM platform’s machine learning algorithms have been better trained to analyze the health data of more diverse populations. Meaning, no matter where in the world this family had been trying to seek answers, their clinical researchers would have benefited from more accurate analysis of data that reflected who they are at the core of their genomes.
Every person is unique. We can clearly see this within the more than one million genomic profiles analyzed by our technology. This is why since day one SOPHiA GENETICS has adopted a global approach and placed it at the core of our company’s DNA. We strive to enable better research and health data analytics for all, no matter where they live or where they come from.
If you’d like to learn more about the SOPHiA DDM platform, contact us for a demo or learn more by exploring the many resources of our website.
As an early adopter of our innovative technology, Protean BioDiagnostics’ President/CEO explains how his lab has already benefited from analyzing complex datasets with SOPHiA DDM™ for TSO500.
“Precision medicine is changing quickly. It’s getting more complicated. But it’s amazing to see how far we’ve come in a short time working with SOPHiA GENETICS,” explained Dr. Anthony Magliocco, President and CEO of Protean BioDiagnostics during a virtual discussion. “Our expertise is in tissues management and integration of data. We have to work with other experts and collaborate with them to fill gaps in our ability.” For this reason and others, Protean has worked with SOPHiA GENETICS to fill those gaps and further enable research possibilities for the clinical research experts they serve.
Protean BioDiagnostic’s vision is to bring precision medicine to all patients, regardless of where they live. That includes empowering community cancer centers and smaller labs (sometimes in very remote areas) that traditionally lag behind in developing digital workflows. Their patients could greatly benefit from the latest and greatest technologies. This is where the two companies have common goals, to evolve the landscape of medical research through Data Driven Medicine.
“SOPHiA DDM™ is a knowledge management system. It's able to help us curate the data and is continuously updated with connections to world databases. It’s a very important tool for us and our partners to simplify the analysis of complicated data. It allows our customers to better analyze the results from genomic profiles as well as clinical trial matches, which I feel is an often-unmet need in research,” said Dr. Magliocco.
Not every hospital system or regional lab has the storage capabilities for large sets of data that are generated with more comprehensive panels and testing. For this reason, the two companies’ shared goal of providing smaller labs advanced tools comes with options for cloud storage that are highly protected. Data storage security is a major priority for SOPHiA GENETICS users. We ensure that they are getting HIPAA and GDPR compliant storage capabilities no matter how large or small their lab may be.
“I’ve known SOPHiA GENETICS for a long time,” said Dr. Magliocco. Protean BioDiagnostics had an existing bioinformatics partnership with the SOPHiA GENETICS team and they signed on as an early adopter of SOPHiA DDM™ for TSO500 within the past year. “I became more interested recently with the work they’re doing with large panels. You're dealing with DNA and RNA and now you also have to deal with other markers in the DNA like tumor mutational burden and micro satellite instability. To be able to visualize it and report it back quickly and accurately makes this a very powerful tool. I’m also impressed by the experience of their bioinformaticians and their approach. They’re always trying to move forward. You know that the product will not be the same in a year or two, that it will improve. It’s a true partnership. I’ve been privileged to make suggestions from a user’s experience, and they actually make those new features available.”
The aim of improving technologies for SOPHiA GENETICS partners is to offer deeper, more accurate analysis. Larger panels may cast wider nets, but optimization with patented algorithms through our universal platform gives more actionable insights without sacrificing data quality.
“The real problem is integrating the data, putting it together, and giving it back to them in a way that's manageable so that it can actually guide treatment,” said Dr. Magliocco. “With the Bioinformatics provided by SOPHiA GENETICS, you’re able to pull together tens of thousands of variants, sift through them all, and get the ones that are meaningful. They highlight the significant data with reporting in a way that makes sense, while still allowing you to ‘open up the hood’ and look further, to see where this reporting comes from. They simplify the reporting, but I know that the complex data is still there if I have to go back and look for more answers. This is a great tool for further research.”
SOPHiA DDM™ for TSO500 is a fully integrated bioinformatic workflow (FASTQ to Report) for Illumina TruSight™ Oncology 500 panel. The SOPHiA DDM platform combines analytical performance with streamlined interpretation of complex genomic variants in the context of comprehensive genomic profiling.
Contact us today to book a demo and learn more.
Next Generation Sequencing is rapidly evolving the landscape of clinical research through new, data-driven approaches that require deeper analysis. But much like opening a flood gate, the total power of possibilities NGS brings can be overwhelming. With so much more data to manually sift through, it can take precious time before any actionable insights are gained from sequencing. By leveraging expertly curated evidence, powered by JAX-CKB, and SOPHiA GENETICS' patented algorithms through the SOPHiA DDM platform, users can then use OncoPortal™ Plus to accurately identify clinical associations and actionable biomarker profiles. With new customizable reporting templates, any lab can prepare state-of-the-art comprehensive genomic reports to match the preferences of their clients and oncologists.
Actionable biomarker assessment
There’s a lot that can be learned from analysis that goes beyond simply mining a sample for all available information. We must consider how we review that large mass of data to determine what is important. Think of it like observing a painting at a museum. If you were to only slowly scan the corners of the Mona Lisa, you may miss the subtly in her smile or how she seems to glow in comparison to the background. Like art, some things are better observed with all minute angles taken into much larger consideration.
The larger portrait an analysis paints must be viewed through multiple filters, like lenses with varying scopes of biomarker assessment and scrutiny. For example, a single variant discovered among the data may indicate a possible treatment option, but another biomarker in that sample may contradict its usefulness as a viable therapy. By only offering a micro-glimpse into individual datapoints, traditional/manual reporting is not automatically showing the macro-effect of what that data means as a whole.
Through molecular profiling, OncoPortal™ Plus doesn’t just identify individual variants, it works with you to better classify and interpret co-occurring biomarkers, including wild-type genes, that give researchers a wider, more accurate portrait of options in reporting. This allows for better decision making, eliminating redundant entries that could lead decision-makers down the wrong paths and waste valuable time.
Flexible push-button reporting
With AMP/ASCO/CAP classification of biomarkers, including Tier III (VUS), OncoPortal Plus combines your (secondary) analysis results with the SOPHiA DDM platform knowledge base to allow you to prepare genomic reports with detailed biomarker descriptions. The new, intuitive format displays actionable biomarkers, approved therapies, and a summary of relevant biomarkers that may play further roles in decision making. All of this can be customized with templates that meet the needs of the individual user.
Custom report templates in the Template Manager give plenty of room for molecular labs to configure their genomic reports to match the various needs and preferences of their clients. Combining the power of the SOPHiA DDM Platform with OncoPortal™ Plus then helps the user to interpret genomic findings for your final reports. Once this feature has been utilized in-house, much of the work can be done with just a click, avoiding having to rely on send-out reporting.
Our new offering comes with constantly improved security and technical support, getting you up and running to quickly match your compliance needs. If you’d like to learn more about our products or catch a demo with an expert, just click here and contact us today.
In its very nature, cancer grows and evolves. Luckily, so does science. In order to combat one of the leading causes of death worldwide, doctors, data scientists, engineers, and countless other medical professionals have worked to discover new strategies to improve patient outcomes. Thanks to recent advancements in technology, patients are now receiving more accurate and uniquely personalized care through Precision Medicine. This requires the combination of all available health data in novel ways that doctors can interpret for new actionable insights for their patients. Leading this revolution of “multi-omics” in Data-Driven Medicine is the groundbreaking field of Radiomics.
You can learn more about Radiomics from our experts at RSNA.
Radiomics is the science of “converting digital medical images such as PET or CT examinations and MR imaging, into mineable high-dimensional data,” (1) for medical professionals to use in clinical settings. These are routine examinations that doctors are already performing, which means Radiomics maximizes upon information that’s already being collected. The difference is how that info is used.
By extracting valuable quantifiable data from images that goes beyond what human eyes can detect, SOPHiA GENETIC’s smart algorithms (AI) equip clinical researchers with more detailed and accurate information from their data, including tumor characteristics or clues about the changes in growth following treatment. This goes beyond the traditional RECIST or PERCIST criteria. This offers experts predictive models based on sophisticated computer algorithms.
While Radiomics has become rapidly utilized within the field of oncology, this technology is applicable to all disease domains. With rapid growth in Radiomics technology, there are several immediate benefits and challenges to tackle as it becomes fully integrated into clinical settings.
Here are some of the things you need to know about Radiomics right now:
1. Tumor segmentation is tricky
2. Radiomics improves workflows
3. Standardizing biomarkers is necessary
4. Radiomics improves patient outcomes
5. Sharing knowledge saves lives
Tumor segmentation is one of the most challenging aspects of Radiomics. This is the actual “capturing” of imaging data in which imaging technologies must go beyond simply scanning the diameter of a lesion. This can be quite time consuming and there is a need for a more simplified process that could be achieved through automation.
More intricate details and biomarkers are required to enhance research outcomes. Some experts have defined these new details as “habitats”. Habitats are the area related to a given tumor, including its’ distinct volumes such as blood flow, cell density, etc. Habitats refer to all of the various parts inside and around the tumor. The details of and differences between these habitats can give specific insights into treatment response, such as pseudoprogression (1, 3). The analysis of the distribution of habitats can eventually indicate which tumors will progress more aggressively than others.
Traditional radiology reports are not always able to integrate multimodal biomarkers or capture such a detailed analysis. It’s the combination of the different data sources that unlocks new potential in how we analyze and track the evolution of diseases in ways that experts were never able to before.
Every industry is looking to be more efficient, but in the medical community, efficiency can remove so much of the horrible stress patients face and ultimately save lives. Seeking a more efficient workflow in the clinic continues to be the driving factor behind the development and adoption of Radiomics.
As a necessary pillar of work performed in the lab, medical imaging is routine in cancer diagnosis and determining a patients’ prognosis. Most, if not all, cancer patients will undergo various, standard examinations including CT, PET and MRI at some point early on in their diagnostic odyssey. The beauty of Radiomics is that it doesn’t require any additional expensive or invasive tests to work. It’s easily integrated into the workflow of medical imaging.
Currently, radiologists are overburdened by the ever-increasing demand of medical imaging and administrative charges (2). Reaching well beyond the capacities of dated computer-aided detection and diagnosis (CAD) systems of routine clinical work, Radiomics automates these mundane tasks and reduces the massive workload of radiologists and clinicians. It doesn’t replace their expertise, rather, as clinicians combine their expertise with the technology available, Radiomics will continue to help investigators transform digital images to uncover hidden patterns or specific information elusive to even the most deliberately trained and experienced eyes.
With Radiomics, clinical researchers will have extra time for more cognitively challenging tasks and be better supported to make the best decisions about their patients’ care.
Accuracy, repeatability, reproducibility – these are the main challenges radiologists and oncologists face while analyzing patients’ tumor progression or response. Without Standardization, it can often feel like researchers are speaking different languages when cross-referencing examinations. In some cases, they are literally speaking different languages in their labs, so without standardization of data, things can become confusing.
Efforts like the Image Biomarker Standardisation Initiative (IBSI) are being made to better standardize image acquisition and data extraction. Going through an image slice by slice with manual segmentation results in regions of greater uncertainty compared to the more modern tools available (3). However, as new tools are developed, they come with more methods that are being used to extract features, thus resulting in more room for variables and bias.
There is ongoing debate about automated tools and how to anticipate potential error and risk with their use. It is necessary to continue to perform adequate risk assessment and validation studies in order to make sure the tools in doctors' hands are not only easy to use, but safe. It’s a tale as old as time. As technology evolves, so must its accuracy.
As no technology has ever replaced a doctor's final judgement, expertise or clinical skills, Radiomics simply provides doctors with more tools in their standards of care to treat their patients. Equipped with more actionable insights, doctors can better understand the underlying pathophysiology and choose the right therapeutic strategy for each given patient.
Every day there are new and exciting therapeutics developing in the field of oncology, such as immunotherapy. While the treatments are promising, new innovative tools are required to continue to comprehensively track and manage patients’ care. Radiomics provides clinicians with more dimensions of reliable and valuable data in order to chart out a path to better patient care and treatments that are best for the individual.
With Radiomics, clinicians can identify biomarkers in vivo over time, such as following tumor changes in volume, texture, and many other characteristics that define a response to treatment. As more medical professionals integrate this new technology into their clinic, Radiomics offers an exciting reality of democratized big-data – a future where clinicians can connect their unique patients to other complex cases or to clinical trials around the world.
Responsible data sharing is the greatest challenge for the fast-growing field of Radiomics. There is a huge need to share information across institutions and clinics globally to connect patients to clinical trials or to support clinical decision-making. For AI to evolve, it’s also essential that algorithms are trained on new sets of data all the time. This takes collaboration.
For a dataset to be statistically relevant, a general rule-of-thumb is to have at least ten times more samples than the parameters you’re modeling (5). So, more data can mean better paths to accuracy. However, with the advancement of radiomic technologies, more and more unsupervised, unlabeled, or poorly curated datasets are becoming available. In some cases across the field, we've seen unreliable datasets being used to train automated tools, resulting in ineffective models (6). This is why SOPHiA GENETICS experts work to collect only the highest quality data, constantly examining the efficacy of what we analyze and share on the platform. We ensure that data is only shared in compliance with the applicable laws and requirements, adhering to the most up-to-date international standards.
Radiomics has the potential to offer a democratized data solution for both clinical and research purposes, bolstering a community of experts. Doctors rely on their years of experience and professional networks to decide which treatment will work best for a patient. With all of the targeted therapies available, Radiomics can help define, standardize, and cultivate big datasets for clinicians to tap into for each specific case, eventually connecting patients with similar profiles for treatments or clinical trials all over the world.
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The information included in this presentation has been prepared for and is intended for viewing by a global audience. This blog post contains information about products which may or may not be available in different countries and if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please consult local sales representatives. All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
SOPHiA GENETICS' Radiomics products are for Research Use Only and are not intended for purposes other than research. They are not for diagnostic, therapeutic, or treatment purposes.
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Data Driven Medicine
Did you know that the “DDM” in SOPHiA DDMTM stands for Data Driven Medicine? You may have heard this phrase before. It’s become a common phrase across the industry as medical research evolves in the digital age. DDM is a term used widely these days, but one that SOPHiA GENETICS has championed from the start. Data Driven Medicine is clinical research that can expand the way we approach traditional medicine, powered by deeper data analysis.
The diagnostic journey can be filled with many unknowns for both patients and physicians. These unknowns are hiding amongst a massive expanse of medical data within each of us, waiting to be discovered, to further inform our personal healthcare. Having the most comprehensive amount of medical data properly analyzed and pre-classified in one single space, allows clinicians to arm themselves with the most pertinent information for their patients, ultimately aiding in the fight against concerning diseases.
SOPHiA DDMTM plays a crucial role in advancing medical research. It is an artificial intelligence-powered software as a service (SaaS) platform that’s currently used in hundreds of labs around the world to compile large sets of varying medical data from multiple sources.
Data optimization
Many larger hospital networks are already using instruments that produce these large amounts of medical research data. But many of those tests being run in the labs are becoming more in-depth, more precise, which means professionals are dealing with incredible amounts of new data each day at increasing rates. For this reason, fast and accurate analysis is essential in order to process more and more relevant information for patient research. This level of efficiency can also allow smaller labs to take on much more comprehensive testing, utilizing fewer resources than traditionally required.
Clinical research depends on accurate, reproducible results. What’s often overlooked is how much more productivity can be enabled within a lab when experiments produce consistent levels of accuracy with faster turn-around-times. This takes a lot of fine-tuning of clinical instruments, which can be yet another taxing and time-consuming process. The simple solution is optimized workflows that can quickly discover what users are searching for among their medical data. With SOPHiA DDMTM, variants of interest are discovered by elite algorithms and preclassified for the user so that they don’t have to go digging and sifting through all of the available data. Think of it like trying to find a treasure buried on a beach. It would be a lot easier to use a metal detector than to pick through each grain of sand.
The SOPHiA GENETICS factor
Since our beginnings, SOPHiA GENETICS has taken a global approach to supporting medical research. This means we designed our technology to be able to support many forms of medical data input, making us the universal platform of choice for more than 780 healthcare institutions worldwide. With Radiomics, we’ve taken our first steps toward true multimodal research.
If you’d like to learn more about the SOPHiA DDMTM platform, you’re in luck! You can book a demo right here through our website, or you could come and see us at the upcoming HLTH conference in Boston from October 17-20th.
Instead, it can accelerate and validate researchers’ discoveries for faster application of their hard work to improve patient care. In healthcare, we see a promising step toward better patient outcomes thanks to advancements in machine learning.
AI and NGS
There’s a reason why it took so long for the first human genome to be sequenced. It’s not an easy thing to accomplish without a “roadmap.” When researchers analyze genomic samples, they’re often overwhelmed with countless panels to run, limited time and resources, and a whole ton of data that gets pulled from every single sample. With artificial intelligence, that first “roadmap” discovered in the Human Genome Project isn’t such a long journey anymore. Algorithms can help quickly identify biomarkers within a person’s genomic data that can assist clinicians shed light on many medical mysteries.
Artificial intelligence has been adopted more widely for clinical trials and patient care in recent years thanks to the evolution of NGS (Next Generation Sequencing). Even when it’s not being utilized as the key player to gather important new insights, machine learning applied to Next-Generation Sequencing can better organize and identify variants of interest hidden among background noise. Algorithms trained on specific data points discover and flag biomarkers in new, more efficient ways, down to the exon level. More in-depth output supports better informed decisions.
AI in radiology
Imaging is one of the first tests performed when a patient begins the diagnostic journey. Traditionally, an expert is trained and educated to specifically search for and identify concerning areas or segments of a given medical image. Artificial intelligence in radiology goes well beyond what the human eye can see by analyzing the available data, not just the image.
Through 3D segmentation and visualization, AI can pinpoint concerning features in medical scans faster and more efficiently than the human eye. Recent studies even suggest that AI for radiomics could be used in preliminary interpretations of chest radiographs to address the scarcity of resources, improve accuracy, and reduce the cost of care.
Democratizing AI-powered data for all
Trustworthy AI requires data volume and diversity. The more unique data that the algorithms can train upon, the more accurate they become in searching for relevant biomarkers within a sample. This is why SOPHiA GENETICS created a universal platform that can adapt and evolve with its users. A global community of more than 780 health care institutions have already supplied the SOPHiA DDM™ platform with relevant data, analyzing more than 770,000 genomic profiles. Standardized, pseudonymized, and aggregated data improve our machine learning algorithms to deliver variant detection, analysis, and interpretation that empowers researchers beyond manual investigation. Learn more about SOPHiA DDMTM, combining data from AI in Genomics and Radiomics with our technology by clicking here.
How data accuracy can be improved with better algorithms
Your favorite song is playing on the car radio, but as you drive along, the frequency seems to hit a snag as hisses and pops infiltrate the music. The same song could sound much clearer on a slightly different radio frequency. It just might take some fine tuning. The process of tuning into a specific “clean” frequency is not unique to music on the radio alone. Medical research must be reproducible without the static.
How do we “tune” into data accuracy?
Some of the most concerning diseases of our time can now be studied in ways that scientists had only dreamed of decades ago. Through the evolution of Next Generation Sequencing, data becomes the lifeblood of new clinical research capabilities.
If you think of data accuracy like that radio signal, what you’re trying to do is tune into the right frequency, searching for your intended disease-causing variant. Ideally, your end result is to hear a perfect signal coming through a mess of static. It’s that signal that gives you the most accurate reading for what you’re searching for among the messy noise that’s naturally present in any given sample you may be testing. In order to finetune the end result, you must eliminate what is known as background noise. For NGS, this is a combination of the biases inherit to design. It looks like peaks of signals when visualized. Background noise could come from any outliers and excess datapoints that don’t apply to the research.
How can data be made better?
The new age of Next Generation Sequencing comes with massive amounts of data being analyzed each day at record levels. The amount of background noise in those datasets also increases on a major scale, making it more difficult to reach levels of accuracy that support your research.
Every single step of an experiment can introduce noise to the mix. Luckily, when data is muddied with irregularities captured throughout the analysis, it can also be cleaned. With advanced algorithms and exceptional analytical performance, it’s easier to identify variants of interest or to overcome any corruption of the data quality/accuracy. This is thanks to the ability to look past the “static” of background noise and zoom in on variants of interest with a higher resolution, sometimes down to 2-5 exons.
How can we further data analysis? It’s clear that the initial data capture is far from the final step in your research. In addition to the existing interpretation functionalities such as ACMG automated variant classification, virtual gene panels, and cascading filters as part of our platform, SOPHiA DDMTM for use with KAPA HyperExome offers extremely accurate detection of biomarkers in a single workflow. The solution and our platform include the Familial Variant Analysis (trio analysis) to automatically filter variants based on different inheritance modes. If you’d like to learn more about what we offer, contact us today.
Sometimes referred to as the father of modern computing, Alan Turing planted the roots of artificial intelligence and machine learning. The British mathematician deciphered cryptic messages during World War II, helping the Allies win. Yet, he was considered a criminal for his homosexuality. And because much of his work was classified, he did not receive due praise from his peers.
The vision
According to his biographer, Alan Turing envisioned machines “turned to any well-defined task by being supplied with the appropriate program.” The theories in Turing’s seminal paper, “On Computable Numbers, with an Application to the Entscheidungsproblem,” supported his invention of the universal Turing Machine, an abstract computing machine that provoked the creation of digital computers.
His digital program storage concepts set the framework for machine learning. Today, Turing’s ripple effect echoes through many industries including healthcare. Thanks to Turing’s first steps, computers categorize molecular data from biological samples to determine the root cause of disease.
The early days of machine learning
Turing’s design for the Automatic Computing Engine was based in his belief that humans and machines could think and learn similarly. He saw the cerebral cortex of an infant as an unorganized machine that became more organized through education. He noted that computing is learned and that both machines and humans could be trained to solve mathematical equations. He also thought that technology would one day embody intelligence in an artificial capacity.
Persecution
Turing’s greatest tragedy seemed to come just after he had reached acclaim. His wartime work to decode Nazi transmissions earned him an accolade as Officer of Most Excellent Order of the British Empire. In 1951 he was elected fellow of the Royal Society of London. But in 1952, Turing admitted he was in a relationship with a man. He was convicted of gross indecency and sentenced to 12 months of hormone therapy.
Turing was discovered dead in his bed, poisoned by cyanide in 1954. Officially ruled a suicide, his death is often attributed to an altered state of mind brought on by the hormone treatment. He had been publishing groundbreaking theories on patterns in living organisms — work still inspiring pattern recognition by machines today.
A continuing contribution
In 2009, Prime Minister Gordon Brown apologized for the British government’s treatment of Turing. In 2013, his work was declassified, and Queen Elizabeth II granted him a Royal Pardon. Turing’s theories were radical for his time but truly visionary.
Turing couldn’t have predicted the impact his theories on computing data would have. The Human Genome Project and the advanced computing abilities on which it relied applied Turing’s work in modern algorithms. Now, his concepts — far more than theories — help inform Data-Driven Medicine. To learn more about the patented algorithms of SOPHiA GENETICS applied to genomic and radiomic data, visit https://www.sophiagenetics.com/technology/.
My educational and professional journey is a pretty straightforward one, and would have probably been entirely pursued in my native Canada if it wasn’t for a sudden move that happened during my PhD. Unexpectedly, my PhD professor joined the University of Miami and I decided to follow him. This wasn’t planned but it’s probably the catalyst for my current career path.
In Florida, I completed a PhD in Biochemistry and Molecular Biology, investigating the expression and function of long noncoding RNA in amyloid formation, and in that pursuit, I quickly had to involve bioinformatics in my focus. The main motivation for that is a pretty classic tale: as the molecules we discovered in the lab were not well-characterized, it pushed us to develop our own unique NGS and bioinformatics workflow to look at expression and function. While the learning curve was steep (I had never touched a terminal before that), the process was rewarding and it opened up the possibility of pursuing opportunities outside the lab.
I graduated in 2018 and started looking for a job in the field of biology that could fulfill my interests in NGS, bioinformatics and client services.
In early 2019, I heard of SOPHiA GENETICS, a foreign company recently installed in the US and looking to grow a team here. I was up to the challenge and applied to a bioinformatician position. I joined the company on April 1, 2019, as one of the first members of the Data Science team in Boston, MA. Data Science being at the heart of what the company does, the team was already pretty big in Europe, but here on the other side of the Atlantic, everything was to be built from scratch.
Now, two years after I joined, I manage the bioinformatics services team for North America. We are a team of four people and growing, looking to hire more talents. Our focus is quite broad as we support a wide range of products, meaning that our work is never dull. All in all, the inherent nature of what we do allows us to be excited to go to work every day. I have no doubt there are still other elements to discover in the human genome related to human health; that’s what makes me so passionate about my job. The growing adoption of NGS and the discovery of other next generation sequencing technologies are key elements in elevating standards of care for various diseases. Knowing that what I do on a daily basis can impact people as an end benefit and improve their quality of life is immensely rewarding.
When you work for a growing company like SOPHiA GENETICS, another interesting aspect is that it allows you to grow with and within the company. Our core business being so innovative and evolving, there are plenty of learning opportunities That’s why I could quickly evolve in my role and now be in charge of my own team after less than two years with the company.
At SOPHiA GENETICS, the way we work is simple: we focus on the finish line. When you know you can contribute to something greater than yourself that can benefit other people, that’s when you can thrive to be at your best level. We push boundaries by being always at the forefront of innovation with our solutions, while being responsive to the needs of our users. But all of this wouldn’t be possible without the help and mutual support of a great team. People, colleagues, teammates, are a big part of who we are as a company. I’m surrounded with supportive, motivated, hard-working and passionate people at all levels; this creates an authentic company culture that boosts all of us, every day.
SOPHiA GENETICS products are for Research Use Only and not for use in diagnostic procedures unless specified otherwise.
SOPHiA DDM™ Dx Hereditary Cancer Solution, SOPHiA DDM™ Dx RNAtarget Oncology Solution and SOPHiA DDM™ Dx Homologous Recombination Deficiency Solution are available as CE-IVD products for In Vitro Diagnostic Use in the European Economic Area (EEA), the United Kingdom and Switzerland. SOPHiA DDM™ Dx Myeloid Solution and SOPHiA DDM™ Dx Solid Tumor Solution are available as CE-IVD products for In Vitro Diagnostic Use in the EEA, the United Kingdom, Switzerland, and Israel. Information about products that may or may not be available in different countries and if applicable, may or may not have received approval or market clearance by a governmental regulatory body for different indications for use. Please contact us at [email protected] to obtain the appropriate product information for your country of residence.
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